Deep neural networks for accurate predictions of crystal stability

Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies wit...

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Autores principales: Weike Ye, Chi Chen, Zhenbin Wang, Iek-Heng Chu, Shyue Ping Ong
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/4d18a79643814bec9e968a7650b9f51d
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Sumario:Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies with very high accuracy.