从头算
齐次空间
可微函数
对称(几何)
计算机科学
物理系统
多样性(控制论)
统计物理学
物理
量子力学
人工智能
数学
几何学
数学分析
作者
Linfeng Zhang,Jiequn Han,Han Wang,Wissam A. Saidi,Roberto Car,E Weinan
出处
期刊:Neural Information Processing Systems
日期:2018-01-01
卷期号:31: 4436-4446
被引量:24
摘要
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
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