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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.04526 |
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| _version_ | 1866914859614994432 |
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| author | Bonneau, Klara Lederer, Jonas Templeton, Clark Rosenberger, David Müller, Klaus-Robert Clementi, Cecilia |
| author_facet | Bonneau, Klara Lederer, Jonas Templeton, Clark Rosenberger, David Müller, Klaus-Robert Clementi, Cecilia |
| contents | Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One of the main criticisms to machine learned potentials is that the energy inferred by the network is not as interpretable as in more traditional approaches where a simpler functional form is used. Here we address this problem by extending tools recently proposed in the nascent field of Explainable Artificial Intelligence (XAI) to coarse-grained potentials based on graph neural networks (GNN). We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. On these examples, we show that the neural network potentials can be in practice decomposed in relevance contributions to different orders, that can be directly interpreted and provide physical insights on the systems of interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04526 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials Bonneau, Klara Lederer, Jonas Templeton, Clark Rosenberger, David Müller, Klaus-Robert Clementi, Cecilia Computational Physics Machine learned potentials are becoming a popular tool to define an effective energy model for complex systems, either incorporating electronic structure effects at the atomistic resolution, or effectively renormalizing part of the atomistic degrees of freedom at a coarse-grained resolution. One of the main criticisms to machine learned potentials is that the energy inferred by the network is not as interpretable as in more traditional approaches where a simpler functional form is used. Here we address this problem by extending tools recently proposed in the nascent field of Explainable Artificial Intelligence (XAI) to coarse-grained potentials based on graph neural networks (GNN). We demonstrate the approach on three different coarse-grained systems including two fluids (methane and water) and the protein NTL9. On these examples, we show that the neural network potentials can be in practice decomposed in relevance contributions to different orders, that can be directly interpreted and provide physical insights on the systems of interest. |
| title | Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2407.04526 |