Artificial Intelligence in Materials Science
Forecasted
This IPAM program advances AI-driven materials science by integrating machine learning with physical laws, simulation, and autonomous experimentation for scientific discovery.
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Funder: Institute for Pure and Applied Mathematics
Due Dates (Anticipated): July 2027
Funding Amounts: Not specified; typical IPAM long programs provide travel support, housing, and stipends for selected participants.
Summary: A program advancing AI-driven discovery in materials science by integrating machine learning with physical laws, simulation, and autonomous experimentation.
Key Information: This is a forecasted opportunity; application portal is not yet open.
Description
This program supports research at the intersection of artificial intelligence (AI), machine learning (ML), and materials science, with a focus on making AI a genuine driver of scientific discovery. The initiative aims to deeply embed AI/ML methods into simulation, theory building, and experimentation, emphasizing the integration of physical laws into next-generation AI architectures. Key research themes include electronic structure modeling, generative AI for inverse design, and the development of self-driving laboratories. The program is designed to bring together experts from applied mathematics, materials science, computer science, and theoretical chemistry and physics to address challenges such as extrapolation, out-of-distribution generalization, and the integration of first-principles simulation with data-driven discovery.
