Symposium BI01—Materials Data Science—Transformations in Interdisciplinary Education

The world is in need of talented people who can design, develop, and drive innovative solutions to global grand challenges. In efforts to maximize talent development, United States institutions have engaged in activities to broaden the participation of women and other persons from groups which have been underrepresented in science, technology, engineering and mathematics (STEM) fields. Despite decades of activity to produce a workforce that reflects the rich diversity of the population, only incremental increases in the numbers of women and racial and ethnic minorities have been realized. Keeping pace with national increases, the Pennsylvania State University (PSU) has engaged in innovative intervention programs that have resulted in notable positive outcomes for women students. These impactful interventions led by the PSU Center for Engineering Outreach and Inclusion (CEOI), enhance the student experience from orientation to career. This center houses the Women in Engineering Program (WEP), the Multicultural Engineering Program, the Campus Outreach Program and the Undergraduate Research and Engagement Office.
Among these important programs the PSU WEP has demonstrated significant positive impacts on the student experience. Cohorts of women who participate in WEP interventions excel academically. These women are retained and graduate at rates higher than cohorts of women who do not participate. Data also shows that women who participate in WEP programs demonstrate higher retention and graduation rates than their male counterparts. Positive intervention programs include WEP Orientation, WEP academic support, first year seminars, networking, mentoring, and professional development. Further, the positioning of WEP in CEOI enables this program to favorably impact other enrichment programs that reach across inclusion groups. A notable example is Engineering Mentoring for Internship Excellence (EMIX) which serves a broad constituency group.
Realizing that the education of women is one of the most impactful drivers in improving global prosperity and desiring to inspire and graduate the kind of engineers who make the world a better place, the PSU college of engineering is seeking to make more transformational increases in the number of women engineering graduates. This activity leverages the success of WEP and engages academic programs to provide compelling student experiences to draw interest from women and other persons from groups underrepresented in engineering. Success towards equity requires careful review of all programs with an eye towards inclusivity. Advancement of gender inclusion also demands that engineering programs identify and articulate the “why” for engineering majors. In addition, success requires that engineering programs establish collaborations with academic programs where gender balance must be aggressively addressed (mechanical engineering, aerospace engineering and computer science and engineering). Enacting efforts to reach aspirational inclusion goals also provides opportunities to identify descriptive programs (such as Humanitarian Engineering and Social Entrepreneurship) in which future engineers can be further inspired to use engineering practice to improve global societal outcomes.
To effectively change the face of the engineering workforce requires that colleges engage men as allies. Experience in gender balanced classrooms and engineering teams in college can help produce professional colleagues who interrupt many of the challenges currently faced by women in working environments. In this presentation we will discuss the success of WEP programs. We will describe efforts of the Gender Equity Initiative to increase the number of women to reach 50% of engineering graduates by 2026. We will also discuss new programs to help engage and graduate men and women who advance equity as allies for inclusion in the global workforce.

Establishing Identity is at the core of the Chickering model of professional development and prior work has shown that underrepresented engineering students are more likely to be retained and graduated if students identify as a member of their major. In this work, we use a one-assignment intervention in a Junior-level materials thermodynamics course to test the hypothesis "Writing a true, personal story about a time thermodynamics happened improves self-identification as a materials scientist." We measure student attitudes with a Likert-scale survey before and after the assignment. Preliminary results show that of all the attitudes surveyed, the only measurable change is an increase in agreement with the statement "I identify as a materials scientist."

We discuss the impact of hosting a public Story Collider show with stories curated from the in-class assignment. We find that the attendees of the storytelling show were surprised to be emotionally affected by student stories, that the event catalyzed department discussions for how to better support students, and provided a unique forum for engagement between students, facutly, and the public. In aggregate, we find that narrative-focused activities have high potential to improve student self-identification with their professon through metacognition, with potential for increased retention of underrepresented engineering students. In parallel, the public storytelling events hold promise for improving culture, climate, and caring between stakeholders in a materials science and engineering department.

During summer 2018, the Princeton Center for Complex Materials gave 16 underrepresented high school students from Trenton and Princeton, New Jersey, the opportunity to learn materials science and its influences on and from society. Lectures and labs included discussions on sustainability, including the UN’s Sustainable Development Goals, and coding from Princeton University professors and researchers. The Princeton University Materials Academy (PUMA) is an education outreach program for minority high school students and it is part of the Princeton Center for Complex Materials (PCCM), a National Science Foundation (NSF) funded Materials Research Engineering and Science Center (MRSEC). PUMA has been serving the community of Trenton for since 2002 each year providing daily lectures from Princeton Materials Science professors, workshops, tours and access to Princeton University laboratories, a glimpse into a real STEM academic environment. We have reached almost 300 students from 2002-2018, with many students repeating multiple years. 100% of our PUMA students have graduated high school and 98% have gone on for college, compared with the overall Trenton district graduation rate of 48% and a free and reduced lunch of 83%. This year, we discuss new initiatives and partnerships with Princeton’s makerspace “StudioLab”, a Princeton Council on Science and Technology space for collaboration and creation across disciplines (STEM, arts, humanities and social sciences), bringing in a coding and wearable technology production component to the program, while meeting Next Generation Science Standards (NGSS). In addition to this, we will also discuss our launch of a new evaluation system with pre- and post- content and attitude tests. We also plan to share the curriculum online to enhance PCCM’s PUMA reach and to help teachers and high school students at a national level and improve diversity and accessibility in STEM.

Great opportunity exists for the fields of science, technology, engineering, and mathematics (STEM) by expanding the diversity of its workforce. Increasing diversity of an institution has been expressly shown to improve education and increase productivity and profitability. However, the raw numbers of “diversity” only tell part of the story. True opportunity lies in increasing the inclusivity of STEM environments, so scientists and engineers of underrepresented identities are not only present, but welcome and celebrated. A central piece to building inclusion in STEM must be changing the underlying culture, which for too long has been defined by only a narrow slice of humanity. There are many components to changing a culture; this work focuses on one, bystander intervention. Specifically, I will present details of a workshop custom-designed to teach graduate students methods for intervening in instances of disrespect and unprofessionalism. Over the course of two years, approximately 4,000 graduate and professional students at Yale, including 350 in STEM fields, have participated in this workshop. Through facilitated discussion of several tailored scenarios, participants are encouraged to develop a wide range of interventions, so anyone can find methods of intervening with which they are comfortable. By empowering community members to intervene in low stakes situations, they can break down ingrained disrespectful behavior that excludes those underrepresented in the community.

Community colleges and other small institutions lack the research equipment for students to do cutting edge undergraduate research. Moreover faculty tend to have a large teaching load, less time for research and also a lack of committed and/or trained students in the research labs as opposed to larger universities which have graduate students. Prince George's Community College (PGCC), a large urban minority institution, and Gallaudet University, an institution for the deaf and hard of hearing, have partnered with Howard University, an HBCU and R2 research university, for over a decade. Since 2007, three NSF grants have funded 46 ten-week summer intern positions that have been filled by minority students including women. Many students had multiple year experiences and were supported during the regular academic year.

Six faculty members from PGCC and Gallaudet were either involved with the students’ research or the development of educational materials, including a LibreTexts textbook and matsci excelets (interactive spreadsheets), and three new courses were developed while partnering with Howard colleagues. Gallaudet funded the remodeling of its science laboratories. Guidelines and procedures were also developed for dealing with the special needs of deaf and hard of hearing students in the laboratory. Nanotechnology related topics have been included in various courses in chemistry and physics at Gallaudet University. Howard faculty have also served as guest lecturers at PGCC and partners in successful grants from NASA and the Department of Education.

We will discuss the workings of a productive partnership that has given our students unique opportunities including five student co-authors on published papers. Tracking of student to bachelors degrees and beyond will be presented. PGCC and Gallaudet faculty publications with Howard colleagues have also been a productive endeavor, including a case where a Howard faculty member became involved in discipline-based educational research through collaborating with PGCC colleagues. PGCC has received NASA support for further engineering and support course revisions and laboratory equipment. Gallaudet University also has NASA support for research and student internships within the Department of Science, Technology, and Mathematics.

This project has extended over a period of more than a decade. Support from a number of agencies through a series of grants to the partners must be acknowledged. Previous grants include two NSF Partnership for Research and Education in Materials (PREM) awards, DMR-0611595 and DMR-1205608 and an NSF IUSE DUE-152463 award. Continuing support comes from the STC Center for Integrated Quantum Materials (CIQM) DMR-1231319. New awards include NASA MISTEC, and a project to increase use of open source textbooks from the Department of Education.

How good can American science, engineering, mathematics, and technology (STEM) be when we are missing more than two-thirds of the talent? (i.e., everyone who is not white and male) The now-false and tired contention that “the statistics of small populations” is the operative reason for the slow advancement of underrepresented groups (women and people of color) in science and engineering, especially to positions of power and impact, has too often been used to deflect action that would transform the culture of STEM research–intensive institutions to one that adapts to the diversity of scientific talent endemic to all of humankind. Teaching academic survival skills, such as COACh (the Committee on the Advancement of Women in Chemistry) has done in workshops held for over fifteen years, without addressing the still-too dysfunctional culture in which one seeks to thrive has been shown to lead to minimal improvement in recruiting, hiring, and recognizing female academic chemists.^[1] As noted in coverage of these findings: “Perceptions of inequality remained constant across younger and older faculty, racial and ethnic lines, and levels of experience in administration.”^[2] Similar difficulties are apparent among the scientific staff of national/federal laboratories.

So how can we change the world of science? Subvert the standard operating procedure. Create a microclimate that shows—over time—how new patterns of operation and inclusiveness yield productive, innovative science—including incorporating undergraduate researchers for full time (six-to-twelve months) of off-campus research. Use the scientific capital and street credentials accrued over time, thanks to the humane but challenging microclimate and the concomitant research productivity of one's team, to challenge the status quo with reasoned and bold arguments for change. Remember the importance of uppity behavior and applying “tipping point” mechanisms to move beyond initial reactions of dismissal to—over time—accepted inevitability (such as greeted my audacious suggestion in March 2000 to withhold federal funds from non-diversified chemistry departments through application of Title IX).^[3] And do not forget market forces—the most important resource in research is smart, motivated students and the most important product of funded research is not peer-reviewed papers, but the critically thinking graduate. It is time to assemble a faculty diversity index that delineates who enters a group to do research, how long to degree, and where each student goes after leaving the group—all disaggregated with respect to gender, race, and ethnicity. This prize demographic—the STEM majors seeking a research program—can then make an informed decision with respect to which universities and departments and groups win their talents. We can then see who among the lovers of the status quo in the research-intensive universities really wants to play hardball. It is time to “out” the toxic departments and research groups.

^† Rolison heads the Advanced Electrochemical Materials Section at the U.S. Naval Research Laboratory (NRL). The views are those of the author and are not necessarily those of the NRL or the U. S. Department of Defense.
[1] J. Stockard, J. Greene, G. Richmond, P. Lewis, J. Chem. Educ. 2018, 95, 1992–1499.
[2] A. Widener, C&EN 2018, 96(31), 20 (30 July).
[3] D.R. Rolison, C&EN 2000, 78(11), 5 (13 March).

A small percentage of underrepresented minority high school graduates pursue STEM majors. Often, underrepresented minority students are subjected to stereotype threats, such as being labeled as intellectually inferior, purposely not being selected to participate in classroom discussions and a lack of sense of belonging, such as a lack of inclusivity from class peers and academic advisors while matriculating through the academic programs. The Office of Inclusive Excellence and Community Engagement (IECE) has a retention program focused on increasing the retention, self-efficacy and sense of belonging of underrepresented minority students in the College of Engineering and Applied Science.

The retention program consists of 4 main components, the summer bridge program, monthly socials, collaborative math and science courses and Sunday dinners. The Summer Bridge Scholars Program is a 7-week summer bridge program. Incoming first-year students participate in a seven-week bridge program. In the program, the students live on campus and are immersed in a campus experience. They take a full course load of classes including: Calculus/Pre-Calculus, Chemistry, Biology, Engineering Design and English. The students participate in study groups and lunch and learn series to help them prepare for their first-year experience. Students who perform well in their Mathematics, Chemistry and Biology courses receive English credit that go towards their graduation requirements.

In their first year on campus, the students are grouped into a cohort and provided support to transition into their academic careers. They participate in Collaborative Courses which are offered through IECE. These Collaborative Courses including Calculus and Physics supplement their first-year course loads. Through the support of our programming, our students generally perform 10-15 points higher than their counterparts.

The office also hosts a weekly Sunday dinner. During the dinner, the students receive a home-cooked meal and have a chance to network with students in all cohorts. The dinner provides a safe space for students who are often facing stereotype threat and implicit bias in their courses. In addition to fellowship and networking, the students also work in study groups and receive tutoring and academic support.

Monthly socials provide professional development opportunities for students. Students are visited by industry partners to discuss resume writing, interview tips, networking and etiquette. Many of our industry partners use this time to develop authentic relationships that feed into an informal mentoring network. We also use monthly social time for fellowship. Students have had socials highlighted by various activities including basketball and hockey games as well as riverboat rides.

In our presentation, we will discuss each program component, our measures of success which include self-efficacy, retention results and academic performance of our freshman cohort.

Diversity and inclusion in science, technology, engineering, and mathematics (STEM) fields is a global issue. The challenging issues facing the world relating to STEM diversity cross national borders and require leveraging the talents of diverse constituents. Active international efforts at inclusive talent development are being undertaken to empower persons from groups historically underrepresented in STEM communities. The US National Action Council for Minorities in Engineering (NACME) reports that in the United States, African Americans are one of the most underrepresented minority groups in engineering relative to their population. This is in spite of the fact that there has been a great deal of progress in “growing African American scientists, engineers, and technologists since the Howard University School of Engineering opened in 1910.” The number of African Americans in engineering at all degree levels is not representative of their percentage in the US population.
In 2012, a workshop on “Ethnic Diversity in Materials Science and Engineering” was co-sponsored by the National Science Foundation (NSF), the Department of Energy (DOE), the MRS Foundation, North Carolina State University, and the University Materials Council (UMC). Comprised of Department Heads, Chairpersons, Directors, and group leaders from academic programs in the materials field in United States, Canadian, and Australian universities, UMC is a forum for sharing best practices related to materials science and engineering (MSE). Focusing on issues affecting recruitment and retention and long-term success in MSE, the workshop participants examined diversity data in MSE departments. According to the US National Center for Science and Engineering Statistics, although African Americans make up 12.2% and Latinos 16.3% of the US population, they received only 2.5% and 5.3% of MSE degrees awarded in 2010, respectively. At the heart of the recommendations to increase retention, recruitment, and career success of ethnically diverse groups were topics with a focus on the following three groups: (a) Individuals, (b) Academic Leaders, and (c) Federal Agencies.
Our goal in this paper is to shift this conversation away from the dire message about the lack of African Americans in the field and focus on positive advancements, namely, the leadership of African Americans in engineering and the role of professional societies in their leadership development. Reflecting on the action plan for ethnic diversity in MSE and STEM, we posit that there is a constituency missing in these discussions, namely, professional societies. While it is critically important to recognize technical achievements and the early champions of change, it is also crucial to highlight the importance of professional societies, and challenge them to develop a greater level of authentic inclusion of African Americans in their organizations. Societies include, but are not limited to, those focused on: (1) advancing diversity and inclusion via empowerment, (2) developing underrepresented groups within specific disciplines, (3) originating and facilitating cross-disciplinary interactions, and (4) leading change in the realm of providing services, information, and tools for stakeholders to create a diverse workforce of engineers. Professional societies can play a pivotal role in the diversification of science and engineering profession and the authentic inclusion of engaged African Americans in the direction of science and engineering disciplines. We will discuss how the development of leaders across academia, industry and governmental entities benefits from the opportunities to grow, serve and eventually lead in student-led, career-enhancing, and paradigm-shifting organizations. This paper highlights the careers of several African American leaders in both industry and academia, including their experiential perspectives on the role of professional societies in their own leadership development.

Undergraduate research has numerous positive outcomes for the participating students ranging from improved performance in classes, higher self-identification as scientists, better graduation rates, and better retention of students from underrepresented demographics. By actively carrying out cutting-edge scientific research, students feel like scientific experts with the ability to tackle difficult problems, and this sense of belonging can be especially valuable for first generation and underrepresented students. Ideally, every first-year student at large research institutions would have the opportunity to be a part of the ground-breaking research happening on their campus; however, the current models of undergraduate research are often unable to provide that experience to first-year undergraduate students. Traditional apprenticeship research positions are designed for advanced undergraduate students who want to make a significant time commitment. Course-based undergraduate research experiences have many positive benefits but often lack the ability to replicate a true research experience. We developed a research group-based undergraduate research program for first-year undergraduate students. Our program allowed 20 students to pursue curiosity driven research using cutting edge data previously collected by our research group. This model is transferable to other research groups, departments, and universities, and the implementation of first-year research experiences would be a significant benefit to the educational experience of undergraduate students, especially for students from underrepresented backgrounds in STEM.

In this program, students were given unanalyzed videos of platinum nanocrystals moving, growing, and attaching in solution collected using a state-of-the-art electron microscope and then were able to investigate and analyze phenomena they found intriguing. We assumed the students had no previous research experience nor knowledge of our research area of nanomaterials, so we taught the background information necessary to complete their projects. After learning the fundamentals of the research area, the undergraduate students began brainstorming interesting questions about the data set they were provided. Students were able to test various hypotheses for how nanocrystals grow and interact and learn how to pivot from a failed idea to a more promising hypothesis. Finally, the students learned how to communicate their results in the form of an academic paper and a poster presentation. Going through the scientific process with a project that was scientifically relevant gave the undergraduate students valuable experience as well as a sense of accomplishment.

The immediate returns, both qualitative and quantitative, show the ability for programs like AGURP to make a difference in the education experience of all first-year STEM students. Roughly half of the applicants who came to the information session were women, and half of the admitted students were women. The students expressed a sense of ownership of their project at the poster session and were proud of their research achievements. Quantitatively, the students expressed significant gains in their self-identification of their research skills from the pre- and post-program surveys. For a program like AGURP to be sustainable, it needs to be positive for both the students and the research groups, and programs like AGURP can be mutually beneficial. The goal of developing a group-based research program is to build an implementable model for other universities, so every first-year undergraduate student aspiring to achieve a STEM degree can feel a sense of belonging through research and increase their persistence rate to graduation.

Outreach camps are an effective route to increasing interest in STEM disciplines, especially for underrepresented groups. They are also common components in the broader impact plans for many early career researchers. However, there is very little basis for understanding which aspects of outreach camps lead to positive outcomes. This is due in large part to the difficulty in comparing existing camps both within specific STEM disciplines and across them. As a result, there is little science-based guidance for the development of effective outreach camp structure or content. We specifically target the process of design in this study. By comparing different methodologies for incorporating design thinking through a qualitative multi-case study across four engineering disciplines, we have begun to assess whether design can be used to positively affect outcomes of STEM outreach camps and provide guidance for outreach development.

The underrepresentation of women and underrepresented minorities (URM, def. non-White, non-Asian) in the engineering pipeline can be attributed to a multitude of factors, including, but not limited to, insufficient preparation and barriers in recruiting into engineering programs at the K-12 level, low self-efficacy, lack of peer support, inadequate academic advising or faculty support, harmful stereotypes of particular groups that influence interactions in classrooms or in peer groups, and a chilly or unappealing climate. The numerous “leaks” in the pipeline along with the sheer variety of established causes lead many institutions, including our own, to take a scattershot and therefore marginally effective approach to promoting diversity and inclusion.

In this paper, we will demonstrate that the Engineering Design Process (EDP) provides an effective framework for goal-setting and developing targeted interventions to substantively advance diversity and inclusion at the undergraduate and graduate levels. We present this work in the form of a 3-year case study at our own institution, a mid-sized, research-focused, land grant university on the US East Coast with student demographics (gender, racial) that reflect national trends. Our EDP framework consists of three steps: (1) Defining the problem; (2) Developing multiple unique and viable concepts; and (3) Iteratively designing, implementing, and refining or abandoning interventions based on formative evaluations. We began in Phase 1 of EDP by defining the issue of diversity at our institution relative to other engineering programs nationally using publically available data on graduation and retention rates. To assess climate issues, we conducted in-depth focus groups of women, URMs, and majority undergraduate and graduate students; and we folded the common themes from these focus groups into annual surveys. These data were used to establish clear metrics and target values for gender and racial diversity across our graduate programs and within each undergraduate department.

Phase 2 of our EDP involved generating multiple unique and viable interventions that addressed the disparities in recruitment and retention identified in Phase 1. Both working groups engaged in a lengthy phases of divergent concept generation by conducting extensive literature reviews, familiarizing themselves with the educational and social psychology literature around diversity and inclusion in STEM, and benchmarking interventions from other institutions. Early concepts were organized using a novel tool that clusters interventions by area of impact (recruitment or retention) and “activation energy” (economic and political cost).

Phase 3 of the EDP, which is ongoing as of this publication, involves implementation and continuous, formative evaluation of interventions clustered into three Specific Aims: (1) Recruitment, (2) Retention; and (3) Cultural Change. At present, the undergraduate working group has operationalized approximately 80% of the specific interventions clustered in Aims 1 and 2 above and 30% in Aim 3. Based on evaluation data, 10% of the interventions have been discontinued, with an additional 20% being substantially modified based on early results. Both working groups are continuously reviewing admissions data to assess impact on recruitment and leveraging focus group and survey data to monitor student climate.

This case study represents the first explicit use of the Engineering Design Process (EDP) to develop a comprehensive plan to address diversity and inclusion at both the undergraduate and graduate levels. Given how daunting diversity issues can sometimes appear, we have found that framing and addressing this issue like engineers and explicitly using the EDP has made the process of goal setting, intervention, and evaluation remarkably clear. The overall process and specific tools presented in this case study may be easily extended to other institutions.

In California, the nation’s most populous state, 56% of its 6.3 million K-12 students are students of color (African Americans, Latinos and American Indian), yet over the last 15 years of rapid growth their enrollment in University of California’s (UC) ten engineering schools has averaged only 12%. As a group, this “underrepresented majority” is the least represented ethnicity in the UC engineering schools. Equally troubling and more critical to the nation is the output side of this dilemma. The nation’s underrepresented engineering students of color—hereafter known as (URES)—do not earn engineering degrees that reflect their representation in engineering freshmen cohorts. The persistent few URES who are admitted to engineering schools—many underrepresented by ethnic culture, neighborhood, low-income and first-generation college status—are enveloped by academic, social/institutional barriers and personal circumstances that result in a national retention to graduation rate of only 40%. Essentially, 60% of the nation’s URES freshmen cohorts do not graduate in engineering. This has been the case since the historic ignition of the minority engineering effort in 1970 [1, 2]. The National Science Board pointed out a key challenge: “Attrition is substantial in engineering, particularly in the first year of college.”[3]. Bright, low income URES and most other underrepresented STEM students of color tend to be educated in lower-performing urban and rural schools that usually provide less rigorous math/science instruction compared to White and Asian students [4]. Rigorous high school math and science preparation is the strongest precollege predictor of persistence and degree attainment and has a profound influence on students’ early performance regardless of race or low income [5]. Also, the intensity of the first year university mathematics and science ‘gatekeeper’ courses is strongly associated with students of color and females leaving STEM majors[4].Through evidence-based investigations, the UCLA school of engineering diversity team identified three critical transition points that lead to high attrition among UCLA URES of color and UC Institutions: (I) Freshmen transitioning from high school, (II) Rising third year URES entering the engineering ‘gatekeeper’ core courses often undergo attrition, and (III) Incoming community college transfer students entering UCLA’s research oriented quarter system. These three major transitions can and do impede academic and social integration, reduce student involvement, vigilance, and perceptions of self-efficacy. The UCLA Center for Excellence in Engineering and Diversity (CEED) designed interventions targeted at these critical transition points and greatly increased URES retention, academic performance and accomplishments. Comparative retention and academic performance data is presented. A new conception that engages URES talent with behavioral, cognitive and affective involvement in engineering education was required. Hence, a theory of change based on Tinto’s Theory of Academic and Social Integration [6], Astin’s Theory of Student Involvement [7] and Bandura’s social learning theories of self-efficacy and collective efficacy [8] informed CEED’s Persistence in Engineering (PIE) model. The PIE retention approach is organized into two strategies—the Critical Transition Program (CTP) and Co-curricular Active Learning Communities (CALC) program establishes an URES academic ecosystem. CTP addresses three attrition points above. The CALC provides community building, professional development, physical space, and mentoring programs. The approach recognizes the URES deficit in educational, social and financial capital and provides interventions to close the gap. However, the PIE strategic focus is to build upon the strengths URES students bring to the university—their ambitions, capacity for hard work, desire to improve social status and affinity for math/science.

At GE Research, our mission is to develop innovative technologies and turn those into real products and solutions in the aerospace, power generation, healthcare, additive manufacturing, transportation, and oil and gas industries. Today, we are combining “physics” with artificial intelligence (AI) and machine learning (ML) to invent new materials, revolutionize manufacturing design, processing and inspection across our industrial portfolio. In this talk, I will provide examples of how data science methods are critical to materials discovery, how we are helping to train our work force, and my thoughts on the critical skills needed for today’s Materials Scientist.

This presentation will provide a perspective on how materials informatics can affect how one trains materials scientists and engineers. I will put this in the context of how we have established an entirely new materials department based on building the foundations of materials science through the lens of information science. The pedagogical foundation of MDI is founded on a data intensive perspective of materials science for the study of materials theory, characterization, synthesis, processing and computational and simulation techniques. Our novel pedagogical framework allows students to learn from that data by deriving information that may be outside the models on which they are based, and use this learning process in order to efficiently and robustly explore the information space that cannot be done by existing models

In this talk, I will present our group's work in developing and disseminating three open-source tools for materials informatics: the atomate software (https://atomate.org) for running high-throughput calculations, the matminer software (https://hackingmaterials.github.io/matminer/) for data mining structure-property relationships, and the matscholar software (https://github.com/materialsintelligence/matscholar) for searching and analyzing text data. I will describe usage of these tools and their impact in helping users learn and perform materials informatics studies. For example, atomate makes it possible for users to generate data with high-throughput density functional theory using a high-level software framework. Matminer implements many of the feature extraction routines reported in the materials informatics literature, making it possible for users to rapidly test many different such algorithms for their study, and also collects together sample data sets for testing new algorithms. Matscholar makes it possible for researchers to search for information across millions of published abstracts. Finally, I will discuss usage of these tools with the Materials Project database and their past and potential future role in educational curricula.

Emerging exascale computing will have a profound impact on materials simulations and machine learning (ML) to enable faster and more targeted material discoveries. This requires a new approach to computational materials science that integrates materials simulations with ML techniques. This interdisciplinary integration, along with the ever-tighter coupling between experiments and simulations, will provide a new platform for ML-enabled materials discovery. Here education and training programs on ML-based methodologies for the materials research community are urgent needs for future scientists and engineers to be competitive in the emerging field.
At the DOE Materials Genome Innovation for Computational Software (MAGICS) Center, we develop open-source materials simulation software, ML tools, training courseware that run on desktops to exascale supercomputers. Center software and databases provide function-property-structure relationships in functional materials to help synthesis and characterization of a wide class of materials. We have provided three hands-on trainings in Center-developed materials software databases so far (106 users from 55 universities, national labs and research institutions), and plan to continue the outreach program at annual MAGICS software workshops. In this talk I will discuss the MAGICS software suite and training courseware, and lessons learned from the software workshops.

This work was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0014607

Transmission electron microscopy (TEM) is a widely used method to observe and quantify the atomic scale structure, composition, chemistry, bonding, electron/phonon distribution and optical properties of nanostructures, interfaces and defects in many materials systems. In addition to observing static structures, in-situ gas and liquid stages now permit dynamic experiments to be performed inside the TEM to observe complex structural and chemical transformations. In all of these cases, the goal in designing and implementing the TEM experiments is to acquire the most information about the sample while the least amount of damaging electron dose is delivered to it –a modern TEM experiment must therefore first determine the “optimal sampling” conditions for the material being studied. In addition, as many TEMs now have direct electron detectors capable of recording 100-1000 images per second or more (resulting in terabytes of data for each experiment), data compression and the use of automated analytics play a key role in interpreting the results of these experiments. The use of compressed sensing and inpainting methods is now being taught as part of the regular senior undergraduate/graduate student course structure in advanced TEM methods. In addition, the use of machine learning to improve the analysis of the sub-sampled datasets and data analytics to extract key parameters from a series of images are also key parts of the course. The sub-sampling/inpainting methodology for optimal sensing is hardwired into the acquisition mechanism of the microscopes used for the practical aspects of these microscopy courses, allowing students to directly modify the means by which images are acquired and test its effect on the speed, resolution and precision of the images obtained/reconstructed/analyzed during their training. In this presentation we will discuss the use of TEM images, and in particular obtaining the best TEM image for the lowest dose, to teach the concepts of compressed sensing, inpainting and machine learning as part of a core materials science method. Overall, we have found that the direct atomic scale images of the structure permit students to quickly get a grasp of the main mathematical and data concepts and how to best implement them in their experimental design. Here we will also discuss the application of these same methods onto other materials characterization techniques (and imaging tools in general), the way that users are trained on those methods, and the precision of the results that are obtained.

Within the fast-growing field of nanoscience and nanotechnology, observing and understanding the relation between physical and chemical properties of nanostructures and their potential applications plays a critical role in fueling and channeling future innovations and future perspectives of the field.
Nanowires, in particular, have been shown to be excellent object of study to explore new fundamental physical phenomena, yet their integration into working devices has not been progressing at the same rate. The potential commercialization of nanowires could hardly be reliant only on a simple count of growing scientific publication. Therefore, it is necessary to expend the view towards patent publications, with a goal to perform detailed comparative analysis of both technological and scientific knowledge. Patent documents are a superb source of technical information that is not published elsewhere, but at the same time are not peer-reviewed documents. For this reason, separate analysis of knowledge stored in scientific literature or patent databases might lead to an invalid path/picture of current nanowire technology development.
Here, we present possibilities of technologies such as the Natural Language Processing and Machine Learning used in the framework of patent and scientific publications search to support heuristic stage of design of new products and technologies. As a result, the content analysis, together with profiling results can shed a light on the overlap or possible gap between scientific discoveries and technological innovations. Finally, the implementation of heuristic methods such as TRIZ, which are proven to be effective tools for problem solving for classical physics and engineering could inspire combination of different methods, approaches and technologies in the field of nanowires, leading towards inventive solutions in the potential new devices, concepts and technologies, especially at the conceptual design stage.

The development of sustainable power sources has attracted significant attentions because of the unsustainability of the traditional energies. Among the next-generation power sources, solar energy with photovoltaic (PV) has been considered as a viable alternative. In particular, hybrid organic-inorganic perovskite solar cells (PeSCs) have been intensively explored due to their high performance, high cost-effectiveness, and broader feasibility. In this study, we investigated the research trends and the collaboration networks with the bibliometric methods based on the scientific publications. For the systematic investigations, all the publications were classified by the publication years and the commercialization factors. From publication years of 2009 to 2018, a total of 6,581 documents were investigated. The commercialization factors were categorized by considering the LCOE and the environmental impact of the PeSCs; cost, efficiency, stability, scaling-up, and public acceptability. In addition, multiple bibliometric methods were used in this study: (1) ‘statistical analyses’ of publication output, (2) ‘topic modeling’ based on the publication abstracts, and (3) ‘social network analysis (SNA)’ of co-authorships at institute level. This strategic analysis will provide various aspects of scientific findings, and facilitate further discussion on the direction of the PeSC research.

Creating robust, simple, and safe material synthesis laboratory activities at an undergraduate level is becoming increasingly important as more undergraduate programs are adopting materials chemistry classes and programs. This presentation will focus on efforts at a PUI to highlight core materials chemistry concepts through hands-on synthetic laboratory activities, including solvothermal syntheses, sol-gel, chemical vapor deposition, powder annealing, synthon design, and computer modeling. We will also discuss opportunities to collaborate with physics departments to incorporate structural and property analyses, for a cross-disciplinary education.

The process of developing new thin film materials can be a rewarding experience as an undergraduate research project exposing students to recipe design for material growth, the intersection of multiple characterization techniques, and incorporating films into useful devices. Additionally, this project is useful in teaching students how to manage and analyze larger data sets garnered from a variety of samples. We are using a series of cermet based selective solar absorbers (SSAs) made with varying ratios of Mo and SiO₂ as a platform to teach students about the many pieces in electronic materials development. These SSAs are tested with several characterization techniques to determine film quality and are then incorporated into a prototype water heater to verify optimization of light absorption and heat retention. We have found this project to be a successful platform for teaching students about the iterative cycle of new materials development.

Rapid advances in information technology have been drastically changing the world. Informational innovations are affecting people's lives divergently and comprehensively, and data sciences, a foundation to utilize these enormous and diverse data, are of increasing importance. Thus, there is an urgent need for educational institutions to train both experts in data science and to train all types of scientists to have data science skills. Tohoku University has made specific efforts on the education in data science. The “International Joint Graduate Program in Data Science (GPDS)” and “Data Sciences Program (DSP)” designated for the international students are data science education programs running in Tohoku University . The former targets current students in the university while the latter recruits overseas students and these programs are operated in a coordination. The foundation of data science is undoubtedly statistical analytics and computer science. However, because of the highly-diversified nature of real-world problems, a data scientist cannot be successful with only a focus in a specialized field such as computer science alone. It is expected that data scientists should be easily able to cooperate with experts across many fields in diversified team research projects. In this regard, GPDS and DSP are operated both internationally and interdisciplinarily, in cooperation with overseas universities and other graduate schools in the university.
GPDS and DSP has been focusing on three in important abilities for data scientists. 1) Problem-finding: ability to find a problem from a transdisciplinary perspective and to create a process of solution. 2) Data analysis: ability to analyze the big data to extract the essential information for the problem-solving across diverse fields such as Materials Science and Life Science. 3) Technology architecture: ability to appropriately organize the technological infrastructure necessary for the problem-solving. Based on this direction, GPDS and DSP have been offering lectures, which cover from basic analysis methods to state-of-the-art research topics of data science. Besides the lectures, the educational programs has practical, step-wise training courses. First one is a start-up exercise for beginners of the computer programing, where students learn how to use Linux and Python. Second one is an intensive class based on drills in data analyses, from basic numeric calculation to specialized methods, such as approaches using complex machine learning models. Students successively work out problems, while instructors inspect the answers and codes from each student. By this process, instructors are able to detect weak and strong points of individual students, and to guide a student toward an efficient learning approach. The last one takes project-based learning (PBL) approach. Students are grouped into interdisciplinary teams and each team has heterogeneities in ability in computer programming, fields of specialty and degrees. In the course, students solve realistic problems using real-world data. In previous cases of this PBL training, students with computer skills tended to play a leadership role in a team. For these advanced students, the training course could be an opportunity to fulfill their ability throughout solving complicated real-world problems and to strengthen their expertise by guiding other un-trained team members. Solving a problem in coordination with other team members is definitely an important skill for data scientists and the course give meaningful experience for all students.
In these courses, students deal with various kinds of data from diverse fields, which include material informatics, materials science, natural language processing, plant engineering, blockchain, advertising, biology, bioinformatics and etc. An essential aspect of this learning is the combination of real-world, messy, datasets, and domain scientists, such as Materials Scientists, as critical team members.

Literate computing weaves a narrative directly into an interactive computation. Text, code, and results are combined into a narrative that relies equally on textual explanations and computational components. Insights are extracted from data using computational tools. These insights are communicated to an audience in the form of a narrative that resonates with the audience. Literate computing lends itself to the practice of reproducible research. One may re-run the analyses; run the analyses with new data sets; modify the code for other purposes.

This presentation will take one through the steps associated with literate computing: data retrieval; data curation; model construction, evaluation, and selection; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.

As a specific example, a data narrative will be built showing how one might build predictive models for the prediction of band gap energies. Reports will be presented as (1) an HTML file, (2) a PDF document (in a format acceptable for journal submission), and (3) a slide presentation.

The presentation will have three main foci:
(1) infrastructure: instantiating the computational environment; loading packages; loading data
(2) computation: data curation, transformation, and analysis; model construction and evaluation
(3) communication: creating tables, charts, and graphs; weaving all components into data narrative

At the presentation’s conclusion attendees will have walked through exercises that may serve as templates to be used with their data as they build their data narratives.

The R and Python ecosystems will be used throughout. All data, code, and text will be made available.

Graduate education is poised to undergo significant changes as we consider how best to prepare students for a dynamically shifting workforce. Areas of high national need often require scientists and engineers that can work in interdisciplinary/convergent spaces with strong, career-aligned skillsets. This presentation will focus on two National Science Foundation programs within the Division of Graduate Education that support new educational models and approaches. The National Science Foundation Research Traineeship (NRT) program was designed to broaden graduate student career pathways and preparation, foster lasting institutional changes in graduate training, and support interdisciplinary to convergent STEM research. The program currently supports 68 projects across 33 states and territories; each project has a budget of approximately $3,000,000. The Innovations in Graduate Education (IGE) program supports studies to test new approaches and generate the knowledge to identify best practices in graduate education. IGE awards are focused, educational research projects with budgets ranging from $300,000-$500,000. Taken together, these programs are testing new models and approaches that will train inclusive cohorts of STEM graduate students with the appropriate career-aligned skillsets to productively move into diverse career pathways. The presenter is the Lead Program Director for both NRT and IGE. PIs interested in learning more about these funding opportunities are encouraged to attend. Program objectives and exemplary projects will be discussed, and time will be included to answer programmatic questions.

The Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the integration of experiments and simulations within a data-aware/enabling framework. To realize this vision, MGI recognizes the need for the creation of a new kind of workforce capable of creating and/or deploying advanced informatics tools and methods into the materials discovery/development cycle. To meet this need, an interdisciplinary team at Texas A&M University has developed an interdisciplinary program that indeed goes beyond the prescriptions set forth by the MGI as it incorporates the discipline of engineering systems design as an essential component of the new accelerated materials development paradigm.
The Data-Enabled Discovery and Development of Energy Materials (D3EM) program, funded by the NSF Research Traineeship (NRT) program out of the Division of Graduate Education has enabled the creation of an interdisciplinary graduate program at the intersection of materials science, informatics, and design. The program consists of an interdisciplinary curriculum consisting of cross-disciplinary components in the three fields of materials, informatics and design, followed by an interdisciplinary integrative course that has, as its goal, the solution of a real-world materials discovery/development problem motivated by industry or national laboratories. In addition to the technical component of the curriculum, the D3EM program includes a comprehensive professional skill development syllabus that includes career path planning, communication skills, collaboration, leadership as well as an intensive technical writing program based on the POWER method pioneered by the College of Education and Human Development of Texas A&M University.
The D3EM program was created a partnership between TAMUs Center for Teaching Excellence and six departments across the Colleges of Science (Chemistry and Physics) and Engineering (Mechanical Engineering, Chemical Engineering, Electrical Engineering and Materials Science and Engineering) and is at its mid point in its time frame. In this talk I will discuss in detail the pedagogical model underpinning the curriculum as well as different aspects of the program as they relate to fostering interdisciplinarity not only within student participants but also among the entire student cohort and participating faculty.

International Data Corporation predicted that 60% of organization in 2021 will use machine learning approaches for more extensive data analysis and insights. However, traditional materials engineering education on the undergraduate and graduate levels falls short to address this need. In this talk, I will discuss how NCState response to this shortcoming. For graduate education, we introduced the a graduate certificate which isdesigned for interdisciplinary graduateeducation at the intersection of materialsscience, engineering, and data science withthe aim of preparing the next generation of materials engineers given the growingdemand for data-science skills andknowledge of the artificial intelligence. The skills and knowledge obtained here will serveas foundation for the understanding of materials informatics and high throughputmaterials discovery that will improve a graduate student’s career prospects. To address the immediate knowledge gaps in graduate and undergraduate education, we introduced a general hands-on introductory class on Materials Informatics with the aim to introduce the emergent field of materials informatics and current approaches that employ informatics and experimental and computational data to accelerate the process of materials optimization, discovery and development. The goal of our efforts was to prepare students to move into career positions that require a basic comprehension of data science techniques as applied to materials science and engineering problems.

Nowadays, the term nanotechnology is strongly present in many areas of science and engineering. Nanotechnology can be defined as a set of disciplines focused on the study, manipulation, and control of matter at the atomic and molecular level, in order to exploit the properties that it presents in the nanoscale, to generate functional materials with physical and chemical attributes that exceed those we know today. However, after several decades of such research efforts, which are the actual industrial applications of nanomaterials? Which nanomaterials are industrially relevant? What should academic materials nanotechnology research focus on? Which are the main aspects that should be addressed by materials undergraduate students in the field? Despite its popularity in both, academics and the mainstream media, nanotechnology is not sufficiently addressed in the classrooms of Chemical Engineering, Chemistry and other Materials Sciences related undergraduate programs. Materials education and related disciplines have evolved slowly in Mexico because the discipline curriculum remained with no significant changes for almost four decades. One of the primary challenges in current undergraduate materials courses is to incorporate topics, such as Materials Data Science, which are related to several technologies that have enabled the massive production, analyses, and management of scientific data.
The fourth Industrial Revolution (IR4.0) is a technological shift driven by the emergence of robotics, Big Data, Internet of Things (IoT), Smart Manufacturing and Cloud-based Manufacturing. The most important elements of this technological era are machines, devices, sensors, and people, to be in communication with each other through the Internet. Hence, artificial intelligence (AI) and digital-physical frameworks make human-machine interfaces regularly present in our daily life. This new scientific and technological landscape demands a transformation in materials education as new concepts, methods, and technologies not previously taught in college are meant to either substitute or complement the current syllabus. This evolution in materials education is particularly important to learn, discover and design data-driven techniques that will allow future materials scientists and engineers to discover new materials through materials informatics and develop materials by means of machine-learning in order to reduce the time and cost of materials design and deployment.

This work describes the case of study of undergraduate chemical engineering students at the Institute of Materials Research, UNAM, Mexico who engaged in a Technology Intelligence (TI) research project as a learning strategy to analyze the potential applications of nanomaterials at industrial scale through the development of basic Data Science Skills. The objective of the project was to conduct data mining within the cycle of TI in order to collect information, validate and curate data in order to answer the research questions mentioned earlier. The data analysis and visualization enable the students to identify nine areas of the potential application of nanomaterials worldwide at an industrial level in the last 10 years (2009-2019): agriculture, biomedicine, construction, cosmetics and healthcare, electronics, energy, food technology, optics and optoelectronics, and textiles.

By engaging undergraduate students in a data-driven project, students developed basic research and data science skills and transformed their attitude and perception towards the conception of nanotechnology, into a novel and attractive approach by linking fundamental knowledge with state-of-the-art research. Additionally, this educational experience allowed them to acquire other important abilities as decision making, data mining, data curation, data analyses, data visualization, detection of relevant correlations and communication of results.

Data-driven materials design informed by legacy data-sets can enable the education of a new workforce, promote openness of the scientific process in the community, and advance our physical understanding of complex material systems. The performance of structural materials, which are controlled by competing factors of composition, grain size, particle size/distribution, residual strain, cannot be modeled with single-mechanism physics. The design of optimal processing route must account for the coupled nature of the creation of such factors, and requires students to learn machine learning and statistical modeling principles not taught in the conventional undergraduate or graduate level Materials Science and Engineering curricula. Therefore, modified curricula with opportunities for experiential learning are paramount for workforce development. Projects with real-world data provide an opportunity for students to establish fluency in the iterative steps needed to solve relevant scientific and engineering process design questions.

Exploratory data analysis (EDA) coupled with data-driven modeling allows new researchers to quickly orient in a field and gain insight into how and why decisions were historically made. The alloy development in 9-12wt% Cr martensitic steels has been of ongoing for thirty years. EDA quickly highlights the trade-off between short-term strength and long-term creep stability with increasing chromium concentration [1]. Though this knowledge can be gained from careful study of the literature, EDA allows the researcher to gain this institutional knowledge in short order without having to know what or where to look for first in the literature. This reduces the training cycle time and has implications to all R&D sectors facing knowledge transfer challenges as the “Boomer” generation retires.

Data-driven process|structure|performance (P|S|P) modeling provides insights into the oft-competing mechanisms that must be considered and optimized in the design of processing routes for new materials and design performance requirements. P|S|P modeling requires a foundation in statistical principles to assess the significance and quality of the findings. The constraints of different modeling techniques allow researchers to infer different physical qualities from the models [2].
Limitations of legacy data teach students the importance of statistical study protocol development. How to design the next study to mitigate issues of uncertainty and leverage prior knowledge to optimize inference gained from the modeling efforts? This goes hand-in-hand with the development of high-throughput experiments for microstructural characterization and mechanical behavior. For example, the study protocol to deconvolute the time-temperature effects on the kinetics of precipitate formation requires multiple processing steps to extract useful metrics. Next, robust data-science algorithms for analysis of microstructure [3], and mechanical performance [4] are needed to efficiently probe the design space. The approaches are useful for establishing protocols for the procurement of new databases of structure and performance for the process development of alloys from conventional and additive manufacturing.

1. Verma AK, Hawk JA, Bruckman LS, et al. (2019) Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels. Metallurgical and Materials Transactions A 50:3106–3120.
2. Verma AK, Huang W-H, Hawk JA, et al. Screening of Heritage Data for Improving Toughness of Creep-Resistant Martensitic Steels. In-prep
3. Smith TM, Senanayake NM, Sudbrack CK, et al. (2019) Characterization of nanoscale precipitates in superalloy 718 using high-resolution SEM imaging. Materials Characterization 148:178–187.
4. Senanayake NM, Yang Y, Verma AK, et al. (2019) An Automated Technique to Analyze Micro Indentation Load-Displacement Curve. Reno, NV

The morphologies of precipitates in metal alloys can often be complex due to competing mechanisms such as anisotropic interfacial energy and misfit strain energy. For these systems, multiphysics computer simulations can be powerful tools since researchers can artificially “turn off” individual mechanisms while holding all other variables constant. For instance, this approach has been used by investigators to elucidate the effects of misfit strain, interfacial energy, and anisotropic growth rates on experimentally observed precipitate morphologies in magnesium-rare earth alloys. Unfortunately, the inherent complexity of these computer simulations often makes them inaccessible for novice learners.

For educational uses, we have published a tool on nanoHUB that simulates two-dimensional equilibrium morphologies of an isolated precipitate within a matrix. The simulation tool, available at https://nanohub.org/tools/prismspfmisfit, is implemented using a black-box approach with a graphic user interface (GUI). To run a simulation, a user only needs to provide material parameters such as anisotropic interfacial energies, anisotropic misfit strains, elastic moduli, and Poisson’s ratios for the system. Underlying the simple GUI is an application built using PRISMS-PF, an open-source phase field framework that uses the finite element method to solve phase field equations to predict microstructural evolution. The nanoHUB tool launches a finite element simulation for a precipitate in a matrix, which is then evolved until it reaches equilibrium. The equilibrium precipitate morphology is output to the user, along with the dimensions of the precipitate and the volume fraction, for further analysis.

We use this online tool to help materials science and engineering students develop expert knowledge on the fundamentals of precipitate morphology. One trait of a subject-matter expert is the many deep connections between the facts they have learned, such as the interplay between interfacial energy and misfit strain energy. To develop these connections in novice students, we have developed an inquiry-based teaching module. Students are presented with a micrograph of metallic precipitates and are prompted to explain the shape and the aspect ratio based on misfit strain energy and anisotropic interfacial energy. Using the PRISMS-PF tool, students run simulations that vary the misfit strain and anisotropy to build mental models of these effects and ultimately identify the dominant mechanism(s) in the precipitate morphology. Finally, we present an evaluation of metacognitive questions that are used for formative assessment of this activity.

The growth of X-ray brilliance and extremely quick snapshots allied to advances in machine learning create new opportunities to streamline the description of materials structures as part of the design of new compounds. From industry to national laboratories, X-ray imaging has become fundamental to measure the function and resilience of new materials and for probing dynamic properties. However, the analysis of these rich datasets at scale requires further research in automation that combines computational and experimental methods.

A major challenge is to couple increasing data rate experiments to new data science algorithms in support of quantitative image analysis that can automatically drive the scientific discovery. Our efforts in deep learning applied to image representation and structural fingerprints have made sample sorting and ranking possible, allowing automated identification of special materials configurations from million-sized databases. These complex networks recognize events from data gathered in two regimes: experimentally and by simulation. While such methods successfully bypass hand-engineered features, their full extension to three-dimensional imagery seldom meets standards that are comparable to manual curation. Additionally, labeling large datasets of 3D data is practically impossible.

For example, the inspection of material deformation using X-ray attenuation contrast data from microtomography often generates two thousand cube voxels per volume. The issue is that the creation of millions of labeled volumes means manually handling eight billion voxels per time step for one experimental setting. Therefore, our research efforts also include the creation of the next generation curation tools based on advanced computer vision algorithms addressing fundamental problems, such as multiresolution algorithms for image segmentation (e.g. graph-based classification and convolutional neural networks), stereological analysis, and enumeration of particles within microtomography imagery.

The contributions of our team include: (a) the development of numerical schemes to analyze data that stem from physical experiments; (b) the construction of new software tools to empower materials scientists and constrain parameter space, particularly given prior knowledge from experimental settings; and (c) the reproducibility of experiments by recognizing the importance of open-source codes and availability of benchmark datasets of scientific images coming from advanced instruments.

This talk will present computational tools for recognition of patterns that occur in scientific images, both coming from synchrotron-based X-ray instruments and simulation through HPC codes. This talk will include scripts for visual analysis and interaction with extracted 3D geometries to be shared with the audience, which will be illustrated on scientific imagery from open-data projects. Use-cases will demonstrate our advancements on inspection of hierarchical materials that consist of many individual strands, bundled within a matrix to achieve high-strength mechanical properties and durability.

Aluminum nitride (AlN) is a promising substrate material for AlGaN-based ultra-violet light emitting diodes. In the Fukuyama group at Tohoku University, AlN crystal growth methods have been developed [1] with a recent focus on solution growth using a Ni-Al alloy. In order to design this technique, a fundamental study for the AlN formation on a Ni-Al droplet was undertaken. To understand the growth behavior and design an optimum crystal growth technique, an in-situ observation system for solution growth of AlN crystal using electromagnetic levitation [2] has been developed.
As part of the Tohoku University and Case Western Reserve University collaboration in Data Science for Life Sciences and Materials Science, an international and interdisciplinary research project started, focused on statistical significant quantification of AlN formation behavior, spanning nucleation, growth and coalescence so to design and define an optimum crystal growth technique.
The materials science goal was to profile the nucleation and growth rates of AlN crystals on the spherical liquid Ni-Al droplet The study protocol, representing the details of how the experiments are to be run, including varying the temperature, the Ni-Al composition ratio, the N₂ gas pressure, and the static magnetic field (which controls the solution flow in the molten droplet). This design space of the crystal growth predictors allowed us to encompass from high to low nucleation rates, and from hundreds of AlN crystalites on the sphere, to controlled single crystal growth. The high speed cameras recorded the droplet from the top and the side simultaneously, and the video images analyzed in this project encompass over 530,000 single frame images. With this large dataset, analysis was done in our distributed and high performance computing environment at CWRU [3]. Image analysis was performed using Python (v2.7) libraries including Matplotlib, Numpy, Scipy, Pandas, Seaborn, Trackpy with Skimage and OpenCV [4-7].
In addition to the technical details, the code development involved graduate and undergraduate students, distributed and high performance computing, data exchanges of large datasets and producing robust codes with a number of students that can be validated and are sufficiently modular so that the pipeline is multi-functional. For code development over a two year period involved 3 undergraduate (UG) and 3 graduate (GS) students who participated sequentially, so proper code styling, commenting, documentation and Git code versioning were essential. In addition, communication between the image analysis students from Materials Science, Mechanical Engineering and Computer Science departments, was enabled by the students all having taken Applied Data Science courses at CWRU, so that the basics of an Open Data Science tool chain, gave them a common framework for both tools and data analysis project structure.
The project goal of the synergistic process may be advanced by French’s teaching Applied Data Science this summer at Tohoku University, with the goal of establishing a “nuclei” of Materials Data Scientists here, which can “grow” into a robust local community of students applying these new and complementary tools to Materials Science problems.

[1] M. Adachi et al., to be submitted.
[2] M. Adachi et al., SN Appl. Sci., 1(2019) 18.
[3] Yang Hu, et al., A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites, IEEE JPV. 7 (2017) 230–236.
[4] Python Software Foundation: Python 2.7.16. https://docs.python.org/2.7/
[5] E. Jones, T. Oliphant, P. Peterson, others, SciPy: Open source scientific tools for Python, 2001. http://www.scipy.org/.
[6] Stéfan van der Walt, et al., scikit-image: Image Processing in Python, PeerJ. 2 (2014) e453. doi:10.7717/peerj.453.
[7]G. Bradski, The OpenCV Library, Dr. Dobb’s Journal of Software Tools. (2000). https://opencv.org/

Recently there has been an explosion of interest in the application of informatics tools, particularly machine learning, to materials and other domains of science and engineering. A number of features make science and engineering domain specific machine learning applications exceptionally well-suited to undergraduate research. First, relative to many research problems, domain specific machine learning applications are often simple to understand and easy to explore with limited background. Second, research projects in this area develop skills of high-value for future employment or post-graduate education, including technical skills in data science, statistics, programming, and specific domains, as well as broader skills in project management, teamwork, and communication. Third, many problems require only a laptop and free software, or relatively inexpensive computing resources (e.g., a small amount of GPU time). Motivated by the above opportunities I initiated the Informatics Skunkworks¹. The group has a goal of engaging undergraduates in research dedicated to realizing the potential of informatics for science and engineering, with a focus on materials problems. We have had over a 100 participants since 2015, now with typically over 30 per semester. The projects have had significant impact on students, shown most quantitatively by a strong list of conference presentations, published papers, student awards, and student placement in top graduate programs and companies. Three major challenges we face are (i) how to give students enough information to enable research but not so much that they cannot learn it quickly, (ii) how to allow students to make progress quickly without extensive programming or machine learning expertise, and (iii) how to provide high-quality mentoring given constraints on mentor experience and time. To overcome (i) we have developed a set of modules on key machine learning issues (e.g., machine vision or how to use specific codes)² targeted at undergraduates with no background who need to quickly getting a practical working knowledge of the material. To overcome (ii) we have developed the MAterials Simulation Toolkit – Machine Learning (MAST-ML) package,³ along with useful practice datasets,⁴ which allows full machine learning project workflows to be executed from a simple input file with no programming skills and limited machine learning background. To overcome (iii) we are exploring increased program structure and student co-mentoring, but are still far from robust solutions. Our teaching modules and MAST-ML tools allow students to make progress in even just a few hours, supporting not just extended research projects but also class projects and laboratory exercises in this area. In this talk I will describe the mechanics of how we structure the skunkworks, some of the projects we have explored (and their successes and challenges), the resources we have developed to enable this work, and ongoing challenges and opportunities. In particular, I will also discuss our vision for the future and efforts to expand the skunkworks across multiple institutions, and I hope this talk will help start collaborations with others with shared interests to develop integrated efforts going forward.

1. https://skunkworks.engr.wisc.edu/; 2. https://bit.ly/2WyBZW9 ; 3. https://github.com/uw-cmg/MAST-ML ; 4. https://figshare.com/articles/MAST-ML_Education_Datasets/7017254

Materials science is increasingly turning to data science to extract meaningful inference from large data streams. However, collaborations between researchers within the materials and data-science fields face several challenges including language barriers and domain-specific norms and expectations. In this presentation we discuss our experiences with using a “hackathon” to bridge this gap. We discuss two hackathons models in which participants work intensely for a short time in small groups to develop data-science solutions to authentic materials-science problems. The first hackathon model was implemented in an international workshop with established scientists, while the second model was implemented in a local environment comprised mostly of graduate students. In both cases, we found that short periods of intense interaction resulted in productive interdisciplinary teams. In the talk we discuss the advantages of various formats, approaches that were and were not effective, and offer suggestions for future endeavors and best practices.

Josh Tappan, Citrine Informatics’ head of community, will share Citine's approach to materials informatics education initiatives. Citrine has successfully run several hands-on, experiential programs with universities across the country.

Josh will share the details of Citrine’s NextGen fellowship, which, over the course of 3 years, has supported 50+ undergraduate students in materials informatics research projects across the United States, as well as the Mines Initiative for Data Driven Materials Innovation (MIDDMI), a partnership with the Colorado School of Mines, which helped 8 student groups incorporate machine learning and materials informatics techniques into their research. Additionally, he will discuss Citrine's approach to education and curriculum development in the industrial materials community.

Audience members will be able to access some of the open educational resources Citrine developed as part of these programs.

We present a novel interactive sonification interface that allows people with visual impairments to perceive the multi-dimensional scientific data using their auditory sense. People with disabilities are traditionally underrepresented in science, technology, engineering, and math (STEM) fields. Developing new accommodation technology for them is therefore important to motivate their participation in scientific research and education, which could cultivate a diverse STEM workforce and ultimately meet the nation’s STEM workforce needs. The participation of people with disabilities in STEM fields that typically provide a higher-paying and more secure job will also enable them to join mainstream society and serve as role models for people with disabilities and many other underrepresented groups. In this regard, we focus on the people with visual impairment, whose participation in STEM research and education has been especially low because the majority of scientific data acquisition and analysis processes tend to heavily rely on visual perception. We develop a new digital interface that converts the multi-dimensional scientific data (e.g. electron microscopy images) to sound waves, a process called sonification, which allows individuals to perceive and understand the data using their auditory sense. We opted to develop our sonification software for the 6th generation of the Apple iPad. The first prototype of our iPad application is built to sonify a high-angle annular dark-field image of a β-Ga₃O₂ lattice. The image is converted to an intensity matrix whose entries are the pixel intensity of the image in 16-bit grayscale. A portion of the iPad’s screen is mapped to points on the image (e.g. entries of the intensity matrix). Based on the location of the user touch on the iPad’s screen, the associated point on the image is converted to sound through one of two modes of functionality. Our first mode of sonification takes advantage of the human ability for acute pitch discrimination and leverages variations in pitch in order give the impression of vertical location of the sound source. We elected to associate points of the image of higher pixel intensity to higher frequencies and points of lower intensity to lower frequencies. We conjecture that blind individuals would be able to trace their finger along the iPad’s screen and identify irregularities in the β-Ga₃O₂ lattice (e.g. crystallographic defects) that would appear as a distinct, underrepresented range of frequencies in the sonic space generated by the image. Our second mode of sonification utilizes the perceived loudness of a sound, through variations in amplitude, in order to give the user the impression that the sound, produced based on their touch input, is originating from some point in 3D space. This approach to sonification leverages the fact that blind users are more sensitive to “binaural sound-location cues.” We further conjecture that, through this second mode of functionality, blind users will be able to gain a spatial understanding of the β-Ga₃O₂ lattice and detect variations in the number of Gallium atoms per atomic column. Our hope is that this application will serve as a framework for more advanced sonification techniques may be built upon.

Data science and data-driven efforts are making substantial impact on the discipline of Materials Science and Engineering. The underlying techniques and research in this area need to be incorporated into educational efforts and curriculum in order to prepare MSE students to use these methods in their materials work. In this talk, I provide an overview of some of the educational efforts in Materials Data Science at Northwestern. There are substantial efforts in this area in faculty members’ research, and these topics naturally have entered the educational curriculum. There are many examples of the incorporation of computational tools into both the undergraduate and graduate curriculum. These are focused on both the tools themselves, but also on the data and databases that result, and the use of these databases in materials design and discovery efforts. In additional, the Center for Hierarchical Materials Design (CHiMaD) is a center of excellence for advanced materials research focusing on developing the next generation of computational tools, databases and experimental techniques in order to enable the accelerated design of novel materials and their integration to industry, one of the primary goals of the U.S. Government's Materials Genome Initiative (MGI). The research within CHiMaD also provides opportunities for educational impact as well. Finally, we discuss the Integrated Computational Materials Engineering (ICME) Master’s program, in which students participate in interdisciplinary courses and seminars where some integrate machine learning methods in materials projects in collaboration with Computer Science faculty.

Data science arises from advances in computing, communication, and data resulting in the ability to develop data-driven models based on large petabyte scale datasets. These distributed computing approaches, complemented by the ease of acquiring large datasets at petabyte scale, is driving the digital transformations of industry, science and technology and society itself. These approaches, complement the “petaflop” computing characteristic of high performance computing, used in materials science such as the materials genome initiative and integrated computational materials engineering research. One challenge for materials data science is that typically materials science datasets have been small and sparse, in comparison to epidemiological studies in the life sciences.
Data Science combines advances in statistics, computer science and domain science (such as materials science) to enable new understandings through the application of statistical and machine learning and most recently deep learning. Consider “pure” data scientists as specialists in the academic fields of math and statistics, and computer science. A need arises to develop broader data science skills across the workforce to produce T-shaped graduates, with deep skills in a domain science such as materials science, while at the same time having broad skills in data science[1].
In 2013 we launched a 1 year study to design an applied data science (ADS) undergraduate minor, available to students across our university. These ADS students learn programming, inferential statistics, exploratory data analysis, modeling and prediction and complete a semester long data science project[2]. The ADS minor, started in 2015, and has grown to include 100 undergraduate and graduate students last academic year. The ADS curricula is taught using an open data science tool chain focused on open and reproducible science, based on R/Rstudio, Python, Git, Markdown and LaTeX to produce, compilable data analyses. In R, for example, advances such as the TidyVerse package of pipes and pipelined code and GGPlot2 for the grammar of graphics for data visualization are major steps towards realizing Donald Knuth’s vision of literate programming and are well matched to today’s multi-disciplinary team research [3].
For materials data science, we now offer a data science concentration, focusing the ADS courses on materials problems while addressing the core challenges of integrating data science with the physical and chemical sciences foundations of Materials Science. Essential to adoption of data-driven modeling is demonstrating how they do not replace our physical and chemical theories, models, and experimental experience. Instead they are a new tool, adding statistical power and significance, with improved inference and prediction. And these analyses must be subject to robust validation, using training and testing splits of the data.
Materials data science is not only an educational challenge, but also calls for advancing how we perform our research experiments and acquire data for analysis. A study protocol, encompassing the samples, their exposures and the evaluations performed on them, constitutes the basis of the metadata, the predictors and the responses of the experiment. In many experiments, it is possible to augment the experiment with additional predictors measured in sufficient numbers to provide statistically sound results. Having materials scientists knowledgeable about these data issues is an important to advancing our research methods.

[1] Debbie Hughes, Roger H. French, Crafting a Minor to Produce T-Shaped Graduates, (2016). http://tsummit.org/files/T-Summit_Speaker_Abstracts-2016.pdf.
[2] Business Higher Education Forum, Creating a Minor in Applied Data Science | BHEF, The Business Higher Education Forum, 2016. http://www.bhef.com/publications/creating-minor-applied-data-science.
[3] D.E. Knuth, Literate Programming, Computer Journal, 27 (1984) 97–111. doi:10.1093/comjnl/27.2.97.

The accelerated maturation of data science and machine learning tools has stimulated their integration into university curricula. Herein, we summarize certain efforts within the MIT Department of Mechanical Engineering, including: (1) The design and delivery of in-class and online teaching modules to upskill MIT MechE graduate-student researchers in datasci/ML; (2) project-based learning activities, i.e., learning by doing; and (3) outreach beyond graduate and undergraduate curricula. We evaluate the hypothesis that the artful integration of these tools into curricula enhances students' intuition for problem framing and solving, especially in systems with manifold inputs and outputs. We hope that our sharing sparks dialogue about evolving expectations for university curricula, at the intersection between traditional disciplines and these new tools.

Recognizing materials development was advancing slower than technological needs, the Materials Genome Initiative (MGI) advocated an interdisciplinary approach employing an informatics framework in materials discovery and development. In response, an interdisciplinary graduate program, funded by National Science Foundation, was designed at the intersection of materials science, materials informatics, and engineering design, aiming to equip the next generation scientists and engineers with Material Data Science. The curriculum spans three stages: disciplinary grounding, multidisciplinary courses, and an interdisciplinary course and research. Over a two year period, students work with faculty and students from multiple fields, such as Physics, Chemistry, Materials Science and Engineering, Chemical Engineering, Electrical and Computer Engineering, Mechanical Engineering, Aerospace Engineering, and Industrial and Systems Engineering. Envisioning challenges in this interdisciplinary learning environment, distinctive and blended faculty and student intellectual communities enhance interdisciplinary collaborations and communications.

To connect faculty members from different disciplines, a Faculty Community of Scholars offers a biweekly platform to share an interdisciplinary culture, appreciate and formulate interdisciplinary partnerships and collaboration, and maintain a communication cadence between members. For students, the program facilitates various intellectual communities. Student Learning Community and Writing Community meet weekly throughout the first semester of the program. These two intellectual communities not only assist students in the development of transferable skills (interdisciplinary communication and collaboration, critical thinking, ethical behavior, organization and management, and writing), but also devote effort to reduce disciplinary barriers by providing opportunities for students to present their disciplines and research as well as reflect on the gaps among disciplines. Students lacking a solid background in Data Science prior to taking multidisciplinary courses (e.g., materials informatics) and an interdisciplinary course and research (materials design studio), can participate in Statistics and Python Coding Boot Camp led by a senior student. Once students begin exposure to Materials Data Science in materials informatics class and materials design studio, a Peer-Mentoring Group of senior students help students with statistical issues, disciplinary gaps, and interdisciplinary research. Additionally, a peer and senior student led Writing Feedback Group is available to students writing an interdisciplinary research paper. To overcome barriers in the teacher-learner connection, each faculty mentor can facilitate Coffee Talks where both faculty and students interact and learn anew. Students generate topics for collegial discussions about career paths, career preparations, academic publication, dissertation writing and defense, funding, teaching, and so on.

In this presentation, we will discuss the implementation of intellectual communities bridging the components of this interdisciplinary graduate program. We will also consider lessons learned from our unique and innovative amalgamation of learning and teaching strategies and ways in which other institutions can implement similar methods.

Progress in computational materials science (CMS) has recently accelerated the discovery of advanced and novel materials. However, there have been few quantitative analyses regarding the change in the number of doctoral degree holders (doctors) who support and promote CMS in Japan.
We estimated the change in the number of doctors in CMS in Japan during the last 50 years. We analyzed the subjects of more than 150 thousand doctoral dissertations in science and engineering (SE) found in a Japanese doctoral dissertation database using text data mining techniques. The number of doctors in SE rapidly increased during the 1990s due to a reorganization of the graduate school and peaked at approximately 2000. Then, it rapidly decreased due to the recent birthrate decline. However, we found that the number of doctors in CMS continued to increase until approximately 2010. In addition to that, the decrease rate of those after 2010 has been small. Therefore, the ratio of doctors in CMS to those in SE has continued to increase. This indicates that the importance of researchers in CMS has been constantly enhanced and recognized.
We also analyzed the differences and similarities between doctoral theses in research universities and those in education-oriented universities in Japan. As a result of the analysis, we found that research universities are leading the national trend of the number of doctors in SE and CMS and the ratio of the number of doctors in CMS to those in SE. On the other hand, at the education-oriented universities, new doctoral courses have been established since the 1990s. Change of number of doctors in SE (especially in engineering) in education-oriented universities followed those of research universities and national trend. It has increased rapidly, and then has dropped rapidly as same as research universities. However, the ratio of the number of doctors in the CMS to those in SE in education-oriented universities was almost constant.
We analyzed keywords in the title of theses in both universities. We found that keywords related to application and simulation appear more frequently in theses of education-oriented universities. On the other hand, the keywords related to theory appear more frequently in theses of research universities. This indicates that research universities foster the researchers who orient more fundamental research and education-oriented universities foster the researchers who orient more applied research and practical research.