Computational Materials Design with Machine Learning and Atomistic Simulations
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The computational techniques of atomistic simulation and machine learning (ML) offer an avenue to rapidly invent new materials which is vital for addressing pressing societal challenges in health, energy and sustainability.
The synergy with ML enables a new paradigm: surrogate models bypass simulations by interpolating among pre-existing calculations at a fraction of the cost, while embedding physics-based priors in ML ensures robustness and transferability.
In this lecture, Professor Gomez-Bombarelli will present the current progress in enabling end-to-end materials design for multiple materials classes and applications, from heterogeneous nanoporous catalysts to polymer electrolytes for batteries or therapeutic peptide macromolecules.