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Prediction of atomic stress fields using cycle-consistent adversarial neural networks based on unpaired and unmatched sparse datasets
Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design. However, the availability of proper data remains a challenge – often, data lacks labels, or does not contain direct pairing between input and output property of int...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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RSC
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9342674/ https://www.ncbi.nlm.nih.gov/pubmed/35979503 http://dx.doi.org/10.1039/d2ma00223j |