Moving closer to experimental level materials property prediction using AI

While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling...

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Detalles Bibliográficos
Autores principales: Jha, Dipendra, Gupta, Vishu, Liao, Wei-keng, Choudhary, Alok, Agrawal, Ankit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279333/
https://www.ncbi.nlm.nih.gov/pubmed/35831344
http://dx.doi.org/10.1038/s41598-022-15816-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279333/
https://www.ncbi.nlm.nih.gov/pubmed/35831344
http://dx.doi.org/10.1038/s41598-022-15816-0

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