Fueling the Era of Data-Driven Materials Design and Synthesis

Speaker

Kristin Persson
University of California, Berkeley

Abstract

Fueling The Era of Data-Driven Materials Design and Synthesis

Fueled by increased availability of materials data, machine learning is poised to revolutionize materials science by enabling accelerated discovery, design, and optimization of materials. As one of the first and most visible of materials data providers, the Materials Project (www.materialsproject.org) uses supercomputing and an industry-standard software infrastructure together with state-of-the-art quantum mechanical theory to compute the properties of all known inorganic materials and beyond. The data, currently covering over 200,000 materials and millions of properties, is offered for free to the community together with online analysis and design algorithms. Serving a rapidly expanding community of more than 600,000 registered users, the Materials Project delivers millions of data records daily through its API, fostering data-rich research across materials science. This wealth of data is inspiring the development of machine learning algorithms aimed at predicting material properties, characteristics, and synthesizability. However, we note that truly accelerating materials innovation also requires rapid synthesis, testing and feedback, seamlessly coupled to existing data-driven predictions and computations. The ability to devise data-driven methodologies to guide synthesis efforts is needed as well as rapid interrogation and recording of results – including ‘non-successful’ ones. This talk will outline the rise of data-driven materials design, predictive synthesis and showcase successes as well as comment on future directions.

Bio

Kristin A. Persson is the Daniel M. Tellep Distinguished Professor at the University of California, Berkeley and a Senior Faculty Scientist at Lawrence Berkeley National Laboratory. She is the Director and founder of the Materials Project (materialsproject.org) which is a world-leading resource for materials data, design and the training of machine learning algorithms. Among other recognitions, she has received the Cyril Stanley Smith Award and she is a member of the Royal Swedish Academy of Science and of the US National Academy of Engineering. She was named an Office of Science Distinguished Scientist Fellow in 2024. She holds several patents in the clean energy space and has co-authored more than 300 peer-reviewed publications.