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End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
Linfeng Zhang · Jiequn Han · Han Wang · Wissam Saidi · Roberto Car · Weinan E

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #78

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.

Author Information

Linfeng Zhang (Princeton University)
Jiequn Han (Princeton University)
Han Wang (Institute of Applied Physics and Computational Mathematics)
Wissam Saidi (University of Pittsburgh)
Roberto Car (Princeton University)
Weinan E (Princeton University)

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