Dr. Chris Taylor - Large-scale Crystal Structure Prediction: Learning from 1000 molecules and beyond
Автор: AIchemyhubUK
Загружено: 2025-12-15
Просмотров: 30
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Talk Abstract:
Computational molecular crystal structure prediction (CSP) is a mature and powerful tool in materials discovery, able to successfully predict and rank the possible crystal polymorphs of a range of functional materials at increasingly large scale. In this talk, I describe our landmark study carrying out thorough CSP explorations on over 1,000 rigid molecules with experimentally-known forms, demonstrating our CSP workflow's overwhelming success in predicting and ranking known forms, and in rationalising empirical crystal engineering rules. I also demonstrate the potential of such large-scale data generation by presenting a machine-learned energy correction and a message-passing (MACE) neural network potential trained on this data, as examples of the possibilities for employing AI trained on such datasets to empower functional materials discovery.
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