2018/05/31 by Linfeng Zhang, Jiequn Han, Han Wang +3 · 1 citation
Physics and Astronomy · #physics.comp-ph #cond-mat.mtrl-sci #physics.chem-ph
published as Conference on Neural Information Processing Systems (NeurIPS), 2018
arxiv created 2018/12/20 · arxiv updated 2020/07/21
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.