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Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
Linfeng Zhang, De-Ye Lin, Han Wang +2
An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential e…
Adaptive coupling of a deep neural network potential to a classical force field
Linfeng Zhang, Han Wang, Weinan E
An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the mol…
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
Linfeng Zhang, Jiequn Han, Han Wang +3
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular inter…
DeePCG: constructing coarse-grained models via deep neural networks
Linfeng Zhang, Jiequn Han, Han Wang +2
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributio…