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End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
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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.
Forward citations
Cited by 2 Pith papers
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Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential
A neural-network potential trained on DFT data predicts that W-Cu alloys soften with copper content and transition from brittle to ductile around 37.5 at% copper.
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