Analyzing Atomic Interactions in Molecules as Learned by Neural Networks DOI Creative Commons
Malte Esders,

Thomas Schnake,

Jonas Lederer

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone not guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques develop general analysis framework atomic interactions and apply the SchNet PaiNN neural network models. We compare these with of fundamental principles understand how well learned underlying physicochemical concepts from data. focus strength different species, predictions intensive extensive properties are made, analyze decay many-body nature interatomic distance. Models deviate too far known physical produce unstable MD trajectories, even when they very high energy force accuracy. also suggest further improvements ML architectures better account polynomial interactions.

Language: Английский

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks DOI Creative Commons
Malte Esders,

Thomas Schnake,

Jonas Lederer

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone not guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques develop general analysis framework atomic interactions and apply the SchNet PaiNN neural network models. We compare these with of fundamental principles understand how well learned underlying physicochemical concepts from data. focus strength different species, predictions intensive extensive properties are made, analyze decay many-body nature interatomic distance. Models deviate too far known physical produce unstable MD trajectories, even when they very high energy force accuracy. also suggest further improvements ML architectures better account polynomial interactions.

Language: Английский

Citations

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