Penn State's Artificial Intelligence and Materials Forum Distinguished Lecture Series

Join Bryce Meredig, cofounder and chief science officer of Citrine Informatics, for an upcoming talk, "Domain-specific Considerations in Machine Learning for Materials Design," in the AI and Materials Forum Distinguished Lecture Series at Penn State.

This lecture is free and open to the University community, and is supported in part by the National Science Foundation AI Institute Planning Grant.

Abstract: Machine learning (ML) offers a promising path to significantly accelerating the development of new materials. At Citrine, we have found that a materials-tailored approach (rather than domain-agnostic ML) is crucial to success. For example, uncertainty quantification (UQ) can help prioritize candidate materials within vast design spaces, and physics-based simulations can provide valuable training data for transfer learning when experiments are scarce. In this talk, I will outline several examples of materials-tailored ML method development, and also highlight promising areas for future research.

Bryce Meredig is cofounder and chief science officer of Citrine Informatics, a materials informatics platform company, where he leads the External Research Department (ERD). ERD conducts non-proprietary, publishable research with collaborators in academia, government, and industry. Meredig’s research interests include the development and validation of physics-informed machine learning methods specific to applications in materials science and chemistry; integration of physics-based simulations with machine learning; and data infrastructure for materials science. Meredig earned his doctorate from Northwestern University and bachelor of applied science and MBA from Stanford University.