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Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra
[Image: see text] The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models’ predictions remains a challenge. For example, a ri...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591337/ https://www.ncbi.nlm.nih.gov/pubmed/37807700 http://dx.doi.org/10.1021/jacs.3c07513 |