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Chemistry

Machine Learning Force Fields for Electrochemistry

Extend work on ML force fields to charge transfer problems in external potentials, enabling in-silico discoveries in batteries, electrolysis, carbon capture, biochemistry and the origins of life

Resources (3)

Crystallography Open Database
Research and Reviews
Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent
Research and Reviews
Machine learning-aided first-principles calculations of redox potentials
Research and Reviews

R&D Gaps (1)

In-Silico Molecular Simulation Is Slow and Kludgy

Chemistry
In-silico molecular simulation has not received the necessary push, despite the promise of machine learning-based surrogate models. Moreover, advancements in quantum chemistry—both AI accelerated and quantum/ASIC-enabled—remain underexploited.
Chemistry
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