Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

Abstract

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is "first principle-based" in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

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• Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids
• Interlayer coupling driven stabilization and superconductivity in bilayer CoTe${}_2$
• Learning Full Configuration Interaction Electron Correlations with Deep Learning
• Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure
• Machine learning approach for vibronically renormalized electronic band structures
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network
• Intrinsic structure of relaxor ferroelectrics from first principles
• REANN: A PyTorch-based End-to-End Multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems
• AtomREM: Non-empirical seeker of the minimum energy escape paths on many-dimensional potential landscapes without coarse graining