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...

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Detalles Bibliográficos
Autores principales: Dinic, Filip, Wang, Zhibo, Neporozhnii, Ihor, Salim, Usama Bin, Bajpai, Rochan, Rajiv, Navneeth, Chavda, Vedant, Radhakrishnan, Vishal, Voznyy, Oleksandr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
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

Internet

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

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