From Local Atomic Environments to Molecular Information Entropy
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866918364490760192 |
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| author | Croy, Alexander |
| author_facet | Croy, Alexander |
| contents | The similarity of local atomic environments is an important concept in many machine-learning techniques which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve a good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and the best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_09282 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Local Atomic Environments to Molecular Information Entropy Croy, Alexander Chemical Physics Materials Science The similarity of local atomic environments is an important concept in many machine-learning techniques which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve a good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and the best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach. |
| title | From Local Atomic Environments to Molecular Information Entropy |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2401.09282 |