This symposium aims to promote an integrated vision of material design—informed by data and channeled by physics-based simulations. Although numerical simulations have revolutionized materials design, they face several challenges, including high computing cost, limited accuracy, and limited potential for inverse design. Machine learning models also suffer from some limitations, e.g., need for large, consistent, and accurate datasets, questionable extrapolations, potential violations of physics and chemistry laws, and limited interpretability. In that regard, data-driven machine learning models and knowledge-driven simulations have the potential to inform, advance, and complement each other—and to address each other’s deficiencies. This symposium builds on the idea that the lack of meaningful integration between data- and knowledge-driven modeling is a missed opportunity in materials science. This symposium will explore new modeling approaches that seamlessly combine and integrate machine learning and simulations—wherein simulation informs machine learning, machine learning advances simulations, or closed-loop integrations thereof.
University of California, Los Angeles
Civil and Environmental Engineering
Argonne National Laboratory
Advanced Photon Source
University of California, Berkeley
University of Louisville