等变映射
齐次空间
嵌入
钥匙(锁)
计算机科学
范围(计算机科学)
标量(数学)
可扩展性
人工智能
机器学习
理论计算机科学
数学
纯数学
程序设计语言
几何学
数据库
计算机安全
作者
Vu Ha Anh Nguyen,Alessandro Lunghi
出处
期刊:Physical review
日期:2022-04-18
卷期号:105 (16)
被引量:18
标识
DOI:10.1103/physrevb.105.165131
摘要
Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks. These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modeling.
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