DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
@article{Wang2018DeePMDkitAD, title={DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics}, author={Han Wang and Linfeng Zhang and Jiequn Han and E Weinan}, journal={Comput. Phys. Commun.}, year={2018}, volume={228}, pages={178-184} }
269 Citations
Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution.
- ChemistryJournal of chemical theory and computation
- 2021
A new deep potential-range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase is developed and an active learning procedure for robust neural network training is developed.
Graph Neural Networks Accelerated Molecular Dynamics
- Computer ScienceThe Journal of chemical physics
- 2022
A GNN Accelerated MD (GAMD) model is developed that directly predicts forces, given the state of the system, bypassing the evaluation of potential energy and is agnostic to the scale, where it can scale to much larger systems at test time.
A GPU-Accelerated Machine Learning Framework for Molecular Simulation: Hoomd-Blue with TensorFlow
- Computer Science
- 2019
Software that enables integration of a scalable GPU-accelerated molecular mechanics engine, Hoomd-blue, with the machine learning (ML) TensorFlow package should lead to both the design of new models in computational chemistry research and reproducible model specification without requiring recompiling or writing low-level code.
Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture
- Computer Sciencenpj Computational Materials
- 2022
By testing on different molecules and bulk systems, it is shown that the proposed molecular dynamics (MD) methodology is generally-applicable to various MD tasks.
Adaptive coupling of a deep neural network potential to a classical force field.
- PhysicsThe Journal of chemical physics
- 2018
This work makes the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential, by using a force interpolation scheme and imposing a thermodynamics force in the transition region.
On application of deep learning to simplified quantum-classical dynamics in electronically excited states
- PhysicsMachine Learning: Science and Technology
- 2021
Deep learning (DL) is applied to simulate non-adiabatic molecular dynamics of phenanthrene, using the time-dependent density functional based tight binding (TD-DFTB) approach for excited states…
Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution
- Chemistry
- 2021
We develop a new Deep Potential Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed…
DP Train, then DP Compress: Model Compression in Deep Potential Molecular Dynamics
- Computer Science
- 2021
This work reports a model compression scheme for boosting the performance of the Deep Potential model, a deep learning based PES model, and demonstrates that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems.
KLIFF: A framework to develop physics-based and machine learning interatomic potentials
- Computer ScienceComput. Phys. Commun.
- 2022
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- Computer SciencePhysical review letters
- 2018
We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep…
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