Free Energy Calculations using Smooth Basin Classification
arxiv(2024)
摘要
The efficiency of atomic simulations of materials and molecules can rapidly
deteriorate when large free energy barriers exist between local minima. We
propose smooth basin classification, a universal method to define reaction
coordinates based on the internal feature representation of a graph neural
network. We achieve high data efficiency by exploiting their built-in symmetry
and adopting a transfer learning strategy. We benchmark our approach on
challenging chemical and physical transformations, and show that it matches and
even outperforms reaction coordinates defined based on human intuition.
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