Symposium CCC-Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation

Materials by Design is rapidly becoming a reality, and has sparked public interest as evidenced by the Materials Genome Initiative. The past 3 years have seen innovations in strategies and techniques for materials development and discovery, however, there are still a great many challenges to overcome. New tools for high-throughput first-principles theory have resulted in multiple databases for theory based material properties. These database tools have yet to be properly integrated with experimental materials synthesis and evaluation, with theory providing guidance to the experimentalist while experimental data provides necessary input for improved theoretical predictions. This need is spurring new concerted efforts between theoreticians and experimentalists to develop collaborative models and readily accessible databases of both theoretical and experimental results. In the process of developing new integrated strategies, key advances have also been made in the core fields of high-throughput theory, combinatorial materials synthesis and evaluation, data handling, data mining, and design of experiments. This symposium will bring together specialists and newcomers from academia, national laboratories, and industry to present the lattest advances and discuss pressing challenges to high-throughput materials innovation.

Topics will include:

High-Throughput First-Principles Theory and Computation
Materials genome initiative
Materials by design: Strategies and approaches
Theory driven materials development
Transforming data into knowledge
Informatics, data mining, and enabling software
Sharing materials data: Web based portals and tools
Integrating theory and experiment as well as theorists and experimentalists
Materials synthesis for high-throughput combinatorial discovery and development
Strategies and tools for high-throughput materials property measurements
Advancing materials into applications: combinatorial device optimization and design
Sharing materials data: overcoming the ownership issue
Synthesis strategies and tools for quantifying materials behavior at interfaces
Transforming data into knowledge and creating tools for educating the MGI generation workforce
Development of data formatting and measurement standards for combinatorial studies

Invited Speakers:

CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _0 (Bangor University, England)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _1 (Massachusetts Institute of Technology, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _2 (Duke University, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _3 (Carnegie Mellon University, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _4 (Cornell University, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _5 (California Institute of Technology, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _6 (Commonwealth Scientific and Industrial Research Organisation, Australia)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _7 (Citrine Informatics, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _8 (Citrine Informatics, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _9 (University of Maryland, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _10 (Northwestern University, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _11 (Bar-Ilan University, Israel)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _12 (National Renewable Energy Laboratory, USA)
CCC_Integrating Experiments, Simulations and Machine Learning to Accelerate Materials Innovation _13 (University of Colorado, Boulder, USA)