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• W3133762917 abstract "Dynamics of flexible molecules are often determined by an interplay between local chemical bond fluctuations and conformational changes driven by long-range electrostatics and van der Waals interactions. This interplay between interactions yields complex potential-energy surfaces (PES) with multiple minima and transition paths between them. In this work, we assess the performance of state-of-the-art Machine Learning (ML) models, namely sGDML, SchNet, GAP/SOAP, and BPNN for reproducing such PES, while using limited amounts of reference data. As a benchmark, we use the cis to trans thermal relaxation in an azobenzene molecule, where at least three different transition mechanisms should be considered. Although GAP/SOAP, SchNet, and sGDML models can globally achieve chemical accuracy of 1 kcal mol-1 with fewer than 1000 training points, predictions greatly depend on the ML method used as well as the local region of the PES being sampled. Within a given ML method, large differences can be found between predictions of close-to-equilibrium and transition regions, as well as for different transition mechanisms. We identify key challenges that the ML models face in learning long-range interactions and the intrinsic limitations of commonly used atom-based descriptors. All in all, our results suggest switching from learning the entire PES within a single model to using multiple local models with optimized descriptors, training sets, and architectures for different parts of complex PES." @default.
• W3133762917 created "2021-03-15" @default.
• W3133762917 creator A5002697621 @default.
• W3133762917 creator A5026929463 @default.
• W3133762917 creator A5069777955 @default.
• W3133762917 creator A5087321869 @default.
• W3133762917 date "2021-03-03" @default.
• W3133762917 modified "2023-10-07" @default.
• W3133762917 title "Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules" @default.
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• W3133762917 doi "https://doi.org/10.1063/5.0038516" @default.
• W3133762917 hasPublicationYear "2021" @default.
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