AI-MI Seminar Series: Tomás Arias (Cornell University)

Chemistry-Accelerated Machine-Enabled Learning (CAMEL): From Theoretical Understanding and Design to Experimental Interpretation and Discovery

Machine learning is transforming materials discovery, but its greatest impact may come not from applying general-purpose AI to materials data, but from constructing learning methods that embody chemical and materials-physics knowledge. I will illustrate this through interpretable representations; differentiable design using Effective Atom Theory (EAT), differentiable Equiformer fast EAT (DEFEAT), and hereditary EAT (HEAT); Bayesian interpretation of experiments; and closed theory–experiment loops. Demonstrated applications span superconductors, photovoltaics, clean-energy catalysts, ptychography, and superconducting qubits.

Tomás Arias, James Gilbert White Distinguished Professor in the Physical Sciences, Cornell U

Tomás Arias is the James Gilbert White Distinguished Professor in the Physical Sciences at Cornell. His group develops novel first-principles methods linking microscopic computables to scientifically and technologically relevant materials properties, enabling first-in-kind theoretical applications. His work spans electronic-structure theory, quantum materials, electrochemistry, energy storage, catalysis, polymer theory, surfaces, photoemission, and statistical physics of crowds and populations.

Thursday, September 17, 2026, 10:00 – 11:00 am ET · Watch live on YouTube @AIMaterialsInstitute