Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

标题
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
作者
关键词
-
出版物
Journal of Chemical Theory and Computation
Volume 16, Issue 8, Pages 5410-5421
出版商
American Chemical Society (ACS)
发表日期
2020-07-17
DOI
10.1021/acs.jctc.0c00347

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