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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang, Linfeng Zhang, Jiequn Han +1
Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulati…
Deep Potential: a general representation of a many-body potential energy surface
Jiequn Han, Linfeng Zhang, Roberto Car +1
We present a simple, yet general, end-to-end deep neural network representation of the potential energy surface for atomic and molecular systems. This methodology, which we call De…
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang +2
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 careful…
Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, Weinan E
Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult pr…
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E, Jiequn Han, Arnulf Jentzen
We propose a new algorithm for solving parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) in high dimension, by making an analog…