Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917193012215808 |
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| author | Esders, Malte Schnake, Thomas Lederer, Jonas Kabylda, Adil Montavon, Grégoire Tkatchenko, Alexandre Müller, Klaus-Robert |
| author_facet | Esders, Malte Schnake, Thomas Lederer, Jonas Kabylda, Adil Montavon, Grégoire Tkatchenko, Alexandre Müller, Klaus-Robert |
| contents | 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 is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing Atomic Interactions in Molecules as Learned by Neural Networks Esders, Malte Schnake, Thomas Lederer, Jonas Kabylda, Adil Montavon, Grégoire Tkatchenko, Alexandre Müller, Klaus-Robert Chemical Physics 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 is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions. |
| title | Analyzing Atomic Interactions in Molecules as Learned by Neural Networks |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2410.13833 |