Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation
出版年份 2022 全文链接
标题
Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation
作者
关键词
-
出版物
Journal of Physical Chemistry Letters
Volume 13, Issue 18, Pages 4052-4057
出版商
American Chemical Society (ACS)
发表日期
2022-05-06
DOI
10.1021/acs.jpclett.2c00647
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- 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
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started