The Phase Diagram of a Deep Potential Water Model

no code implementations • 9 Feb 2021 • Linfeng Zhang, Han Wang, Roberto Car, Weinan E

Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region.

Chemical Physics

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

1 code implementation • 1 May 2020 • Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles.

Computational Physics

Deep neural network for Wannier function centers

1 code implementation • 27 Jun 2019 • Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car

We introduce a deep neural network (DNN) model that assigns the position of the centers of the electronic charge in each atomic configuration on a molecular dynamics trajectory.

Computational Physics Materials Science Chemical Physics

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

no code implementations • 28 Oct 2018 • Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E

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 energy surface (PES) for the molecular modeling of materials.

Active Learning BIG-bench Machine Learning

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

1 code implementation • NeurIPS 2018 • Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology.

Computational Physics Materials Science Chemical Physics

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

5 code implementations • 30 Jul 2017 • Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E

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.

Deep Potential: a general representation of a many-body potential energy surface

1 code implementation • 5 Jul 2017 • Jiequn Han, Linfeng Zhang, Roberto Car, Weinan E

When tested on a wide variety of examples, Deep Potential is able to reproduce the original model, whether empirical or quantum mechanics based, within chemical accuracy.

Computational Physics