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Strain data augmentation enables machine learning of inorganic crystal geometry optimization
Machine-learning (ML) models offer the potential to rapidly evaluate the vast inorganic crystalline materials space to efficiently find materials with properties that meet the challenges of our time. Current ML models require optimized equilibrium structures to attain accurate predictions of formati...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982222/ https://www.ncbi.nlm.nih.gov/pubmed/36873906 http://dx.doi.org/10.1016/j.patter.2022.100663 |