Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

(2021) Manyi Yang et al.   CATALYSIS TODAY

Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space*

(2021) Wanrun Jiang et al.   Chinese Physics B

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

(2021) John A. Keith et al.   CHEMICAL REVIEWS

Physics-Inspired Structural Representations for Molecules and Materials

(2021) Felix Musil et al.   CHEMICAL REVIEWS

Phase Diagram of a Deep Potential Water Model

(2021) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

(2020) Yuzhi Zhang et al.   COMPUTER PHYSICS COMMUNICATIONS

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Ab-initio molecular dynamics study on chemical decomposition reaction of α-HMX

(2020) Shiquan Feng et al.   CHEMICAL PHYSICS LETTERS

Training atomic neural networks using fragment-based data generated in virtual reality

(2020) Silvia Amabilino et al.   JOURNAL OF CHEMICAL PHYSICS

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

(2020) Denghui Lu et al.   COMPUTER PHYSICS COMMUNICATIONS

Training Neural Nets to Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality

(2019) Silvia Amabilino et al.   JOURNAL OF PHYSICAL CHEMISTRY A

The Onset of Dehydrogenation in Solid Ammonia Borane: An Ab Initio Metadynamics Study

(2019) Valerio Rizzi et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

High Energy Explosive with Low Sensitivity: A New Energetic Cocrystal Based on CL-20 and 1,4-DNI

(2019) Yanwei Tan et al.   CRYSTAL GROWTH & DESIGN

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach

(2018) Yi Wang et al.   Nature Communications

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint

(2016) Omar Valsson et al.   Annual Review of Physical Chemistry

Ab Initio Molecular Dynamics of High-Temperature Unimolecular Dissociation of Gas-Phase RDX and Its Dissociation Products

(2015) Igor V. Schweigert   JOURNAL OF PHYSICAL CHEMISTRY A

Decomposition of a 1,3,5-Triamino-2,4,6-trinitrobenzene Crystal at Decomposition Temperature Coupled with Different Pressures: An ab Initio Molecular Dynamics Study

(2015) Qiong Wu et al.   Journal of Physical Chemistry C

C–N bond dissociation energies: An assessment of contemporary DFT methodologies

(2010) Cai Qi et al.   JOURNAL OF MOLECULAR STRUCTURE-THEOCHEM

Ab Initio Molecular Dynamics Study on the Initial Chemical Events in Nitramines: Thermal Decomposition of CL-20

(2008) Olexandr Isayev et al.   JOURNAL OF PHYSICAL CHEMISTRY B