Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data

9 May 2020 Chi Chen Yunxing Zuo Weike Ye Xiangguo Li Shyue Ping Ong

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data... (read more)

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  • MATERIALS SCIENCE
  • DISORDERED SYSTEMS AND NEURAL NETWORKS