期刊
JOURNAL OF PHYSICAL CHEMISTRY B
卷 124, 期 33, 页码 7284-7290出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.0c06926
关键词
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资金
- National Key R&D Program of China [2017YFA0303500, 2018YFA0208603]
- National Natural Science Foundation of China [91645202, 21722306, 21633006]
- Anhui Initiative in Quantum Information Technologies [AHY090200]
Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.
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