ArticleThe Journal of chemical physics2023
DeePMD-kit v2: A software package for deep potential models.
Article in The Journal of chemical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
96 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- Hydrogen bonding in water under extreme confinement.Nature · 2026Article
- Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multiscale Investigation of Triboelectric Nanogenerator: An Expanded DFT Calculation Based on Machine Learning.ACS omega · 2026Article
- Disentangling cation-polyanion coupling reveals which anion motion dominates cation transport in solid electrolytes.Nature communications · 2026Article
- Ferroelastic hysteresis, shear modulus softening, and the tetragonal↔cubic transition in davemaoite.Science advances · 2026Article
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis.Journal of chemical theory and computation · 2026Article
- Coverage-Dependent Free-Energy Bottlenecks in CO Adsorption and Desorption on Ru(0001).JACS Au · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Manipulating Thermal Transport of 2D MOFs by Hierarchical Structural Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Improvements in chemical reaction pathway exploration algorithms and dataset generation.Scientific data · 2026Article
- Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics.Accounts of materials research · 2026Article
- Ion-disrupted hydrogen-bond networks enable fast water transport under two-dimensional confinement.Nature communications · 2026Article
- Light hydrogen isotopes in terrestrial core.Science advances · 2026Article
- Chemical intuition on bond-dissociation energies as an emergent ability of universal machine-learning interatomic potentials.Nature communications · 2026Article
- Sulfur-Induced Shape Compliance Modulates Fe Catalyst Agglomeration in Carbon Nanotube Growth.Nano letters · 2026Article
- Deep-Learning Enabled Atomistic Understanding of Thermomechanical Behaviors and Fracture Mechanisms of High-Entropy Diboride (HfMaterials (Basel, Switzerland) · 2026Article
- Understanding the precipitation mechanism in pentavalent vanadium electrolytes through deep learning potential molecular dynamics.Chemical science · 2026Article
- Direction-specific enhanced diffusion of CONature communications · 2026Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
47 authors.
Funding
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.