Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-p...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-024-01393-y |
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