Materials Data Science is a cross-cutting research area that, in over a decade, has progressed along the data science continuum: from data science to generate insights on materials phenomena; through machine learning to establish predictions along the process-structure-property continuum; to artificial intelligence to take action on materials design/discovery spaces. Materials Data Science has also fostered further integration of computational and experimental approaches to better understand materials phenomena, impacting research in areas such as data-enabled materials discovery and design, quantification of hierarchical material microstructures, multi-scale computational materials simulations for performance and materials degradation as well as data-enabled materials decision-making.

Given its transformative nature, it is not surprising that Materials Data Science has permeated much of materials-related research activity around the world. However, materials education lags behind but presents an important challenge to the community. The full potential of the Materials Genome Initiative can be realized only through the development of human resources that are not only versed in the concepts and frameworks behind data-science methods but also capable of deploying them to discover and design materials capable of transforming our future. This symposium is thus dedicated to explore the different ways in which Materials Data Science is being integrated into the undergraduate, graduate and continuing education material-focused curricula and to provide a discussion of best practices and “lessons learned” for modernizing materials-science curricula and to identify gaps and additional needs/opportunities in the educational spectrum.

Education simulation standards