skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models

Journal Article · · Journal of Chemical Theory and Computation
 [1];  [2]; ORCiD logo [3];  [4];  [5]; ORCiD logo [6]; ORCiD logo [1]
  1. HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China
  2. Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, P. R. China; Institute of Physics, Chinese Academy of Sciences, Beijing 100190, P. R. China
  3. Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, United States
  4. Beijing Institute of Big Data Research, Beijing 100871, P. R. China
  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China; University of Chinese Academy of Sciences, Beijing 100049, P. R. China
  6. HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P. R. China; Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, P. R. China

Not provided.

Research Organization:
Princeton Univ., NJ (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0019394
OSTI ID:
1977926
Journal Information:
Journal of Chemical Theory and Computation, Vol. 18, Issue 9; ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

References (23)

Machine learning of accurate energy-conserving molecular force fields journal May 2017
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost journal January 2017
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics journal April 2018
Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties journal July 2020
Accelerating atomistic simulations with piecewise machine-learned ab Initio potentials at a classical force field-like cost journal January 2021
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy journal February 2021
Reactive uptake of N 2 O 5 by atmospheric aerosol is dominated by interfacial processes journal February 2021
Signatures of a liquid–liquid transition in an ab initio deep neural network model for water journal October 2020
Phase Diagram of a Deep Potential Water Model journal June 2021
Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics journal December 2020
Structure and dynamics of warm dense aluminum: a molecular dynamics study with density functional theory and deep potential journal January 2020
A comprehensive survey on model compression and acceleration journal February 2020
Back Propagation Neural Networks journal January 1998
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics journal July 2018
Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets conference May 2013
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales journal February 2022
Active learning of uniformly accurate interatomic potentials for materials simulation journal February 2019
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models journal February 2020
Isotope effects in liquid water via deep potential molecular dynamics journal October 2019
Deep neural network for the dielectric response of insulators journal July 2020
Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space* journal May 2021
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation journal July 2013
The individual and collective effects of exact exchange and dispersion interactions on the ab initio structure of liquid water journal August 2014

Similar Records

Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
Journal Article · Mon Mar 01 00:00:00 EST 2021 · Journal of Chemical Physics · OSTI ID:1977926

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Journal Article · Sat Aug 01 00:00:00 EDT 2020 · Computer Physics Communications · OSTI ID:1977926

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Journal Article · Sat Aug 01 00:00:00 EDT 2020 · Computer Physics Communications · OSTI ID:1977926

Related Subjects