Artificial Intelligence in Materials Science
Artificial Intelligence in Materials Science supports researchers combining AI and physical theories for materials discovery, simulation, inverse design, and autonomous experiments to advance scientific discovery.
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Funder: Institute for Pure and Applied Mathematics
Due Dates: April 16, 2027 (Application deadline)
Funding Amounts: Financial support for participation in all or part of the 14-week program
Summary: Supports researchers integrating AI with physical theories for materials discovery, simulation, inverse design, and autonomous experimentation.
Key Information: Full-program participation is preferred; workshop-only applicants must use the relevant workshop application form.
Description
The Institute for Pure and Applied Mathematics (IPAM) at UCLA runs this long program to advance AI from a tool that accelerates research into a driver of scientific discovery. It brings together researchers in applied mathematics, materials science, computer science, theoretical chemistry, and physics to connect first-principles simulation with data-driven discovery, synthesis, and autonomous experimentation.
Scientific themes include data-driven electronic structure, parametrization and surrogates for electronic and atomic degrees of freedom, generative AI for inverse design of molecules and materials, and self-driving laboratories combining robotics with AI. Across these themes, the program emphasizes embedding physical laws and constraints in AI architectures, ensuring reliability beyond simple interpolation, and addressing extrapolation and out-of-distribution generalization.
