Molecular modeling with machine-learned universal potential functions

• URL: http://arxiv.org/abs/2103.04162v1
• Date: Sat, 6 Mar 2021 17:36:39 GMT
• Title: Molecular modeling with machine-learned universal potential functions
• Authors: Ke Liu, Zekun Ni, Zhenyu Zhou, Suocheng Tan, Xun Zou, Haoming Xing, Xiangyan Sun, Qi Han, Junqiu Wu and Jie Fan
• Abstract summary: We show that neural networks can be used to train an universal approximator for energy potential functions. We have been able to train smooth, differentiable, predictive potential functions on large scale crystal structures.
• Score: 15.138489177130511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train an universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large scale crystal structures. A variety of tests have also performed to show the superiority and versatility of the machine-learned model.

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