@Article{Cao_PhysChemChemPhys_2022_v24_p11801,
    author =   {Liqun Cao and Jinzhe Zeng and Bo Wang and Tong Zhu and John Z H Zhang},
    title =    {{Ab initio neural network MD simulation of thermal decomposition
             of a high energy material CL-20/TNT}},
    journal =  {Phys. Chem. Chem. Phys.},
    year =     2022,
    volume =   24,
    issue =    19,
    pages =    {11801--11811},
    annote =   {CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane,
             also known as HNIW) is one of the most powerful energetic materials.
             However, its high sensitivity to environmental stimuli greatly reduces
             its safety and severely limits its application. In this work, ab
             initio based neural network potential (NNP) energy surfaces for both
             {\ensuremath{\beta}}-CL-20 and CL-20/TNT co-crystals were constructed.
             To accurately simulate the thermal decomposition processes of these
             two crystal systems, reactive molecular dynamics simulations based on
             the NNPs were performed. Many important intermediate species and their
             associated reaction paths during the decomposition had been identified
             in the simulations and the direct results on detonation temperatures
             of both systems were provided. The simulations also showed clearly
             that 2,4,6-trinitrotoluene (TNT) molecules in the co-crystal act as a
             buffer to slow down the chain reactions triggered by nitrogen dioxide
             and this effect is more significant at lower temperatures.
             Specifically, the addition of TNT molecules in the CL-20/TNT co-
             crystal introduces intermolecular hydrogen bonds between CL-20 and TNT
             molecules in the system, thereby increasing the thermal stability of
             the co-crystal. The current reactive molecular dynamics simulation is
             performed based on the NNP which helps in accelerating the speed of ab
             initio molecular dynamics (AIMD) simulation by more than 3 orders of
             magnitude while preserving the accuracy of density functional theory
             (DFT) calculations. This enabled us to perform longer-time simulations
             at more realistic temperatures that traditional AIMD methods cannot
             achieve. With the advantage of the NNP in its powerful fitting ability
             and transferability, the NNP-based MD simulation can be widely applied
             to energetic material systems.},
    PMCID =    {PMC9173692},
    doi =      {10.1039/D2CP00710J},
}

@Article{Zeng_NatCommun_2020_v11_p5713,
    author =   {Jinzhe Zeng and Liqun Cao and Mingyuan Xu and Tong Zhu and John Z H
             Zhang},
    title =    {{Complex reaction processes in combustion unraveled by neural network-
             based molecular dynamics simulation}},
    journal =  {Nat. Commun.},
    year =     2020,
    volume =   11,
    issue =    1,
    pages =    5713,
    annote =   {Combustion is a complex chemical system which involves thousands of
             chemical reactions and generates hundreds of molecular species and
             radicals during the process. In this work, a neural network-based
             molecular dynamics (MD) simulation is carried out to simulate the
             benchmark combustion of methane. During MD simulation, detailed
             reaction processes leading to the creation of specific molecular
             species including various intermediate radicals and the products are
             intimately revealed and characterized. Overall, a total of 798
             different chemical reactions were recorded and some new chemical
             reaction pathways were discovered. We believe that the present work
             heralds the dawn of a new era in which neural network-based reactive
             MD simulation can be practically applied to simulating important
             complex reaction systems at ab initio level, which provides atomic-
             level understanding of chemical reaction processes as well as
             discovery of new reaction pathways at an unprecedented level of detail
             beyond what laboratory experiments could accomplish.},
    PMCID =    {PMC7658983},
    doi =      {10.1038/s41467-020-19497-z},
}


@Article{Zeng_PhysChemChemPhys_2020_v22_p683,
  author =   {Jinzhe Zeng and Liqun Cao and Chih-Hao Chin and Haisheng Ren and John Z. H. Zhang and Tong Zhu},
  title =    {{ReacNetGenerator: an automatic reaction network
              generator for reactive molecular dynamics
              simulations}},
  journal =  {Phys. Chem. Chem. Phys.},
  year =     2020,
  volume =   22,
  number =   2,
  pages =    {683--691},
  abstract =     {Reactive molecular dynamics (MD) simulation makes
              it possible to study the reaction mechanism of complex
              reaction systems at the atomic level. However, the
          analysis of MD trajectories which contain thousands of
          species and reaction pathways has become a major
          obstacle to the application of reactive MD simulation
          in large-scale systems. Here, we report the development
          and application of the Reaction Network Generator
          (ReacNetGenerator) method. It can automatically extract
          the reaction network from the reaction trajectory
          without any predefined reaction coordinates and
          elementary reaction steps. Molecular species can be
          automatically identified from the cartesian coordinates
          of atoms and the hidden Markov model is used to filter
          the trajectory noises which makes the analysis process
          easier and more accurate. The ReacNetGenerator has been
          successfully used to analyze the reactive MD
          trajectories of the combustion of methane and
          4-component surrogate fuel for rocket propellant 3
          (RP-3), and it has great advantages in terms of
          efficiency and accuracy compared to traditional manual
          analysis.},
  doi =      {10.1039/C9CP05091D},
}

@incollection{Zeng_2022_Chapter,
  title = {Chapter 12 - Neural network potentials},
  editor = {Pavlo O. Dral},
  booktitle = {Quantum Chemistry in the Age of Machine Learning},
  publisher = {Elsevier},
  pages = {279-294},
  year = {2023},
  isbn = {978-0-323-90049-2},
  doi = {10.1016/B978-0-323-90049-2.00001-9},
  author = {Jinzhe Zeng and Liqun Cao and Tong Zhu*},
  keywords = {Neural network, Potential energy surface, Molecular dynamic simulation, Chemical reaction},
  abstract = {Recently, artificial neural network-based methods for the construction of potential energy surfaces and molecular dynamics (MD) simulations based on them have been increasingly used in the field of theoretical chemistry. The neural network potentials (NNP) strike a good balance between accuracy and computational efficiency relative to quantum chemical calculations and MD simulations based on classical force fields. Thus, NNP is becoming a powerful tool for studying the structure and function of molecules. In this chapter, we introduce the basic theory of NNP. The construction steps and the usage of NNP are also introduced in detail with the MD simulation of methane combustion as an example. We hope that this chapter can help those readers who are new but interested in entering this field.},
}

@misc{g16,
author={M. J. Frisch and G. W. Trucks and H. B. Schlegel and G. E. Scuseria and M. A. Robb and J. R. Cheeseman and G. Scalmani and V. Barone and G. A. Petersson and H. Nakatsuji and X. Li and M. Caricato and A. V. Marenich and J. Bloino and B. G. Janesko and R. Gomperts and B. Mennucci and H. P. Hratchian and J. V. Ortiz and A. F. Izmaylov and J. L. Sonnenberg and D. Williams-Young and F. Ding and F. Lipparini and F. Egidi and J. Goings and B. Peng and A. Petrone and T. Henderson and D. Ranasinghe and V. G. Zakrzewski and J. Gao and N. Rega and G. Zheng and W. Liang and M. Hada and M. Ehara and K. Toyota and R. Fukuda and J. Hasegawa and M. Ishida and T. Nakajima and Y. Honda and O. Kitao and H. Nakai and T. Vreven and K. Throssell and Montgomery, {Jr.}, J. A. and J. E. Peralta and F. Ogliaro and M. J. Bearpark and J. J. Heyd and E. N. Brothers and K. N. Kudin and V. N. Staroverov and T. A. Keith and R. Kobayashi and J. Normand and K. Raghavachari and A. P. Rendell and J. C. Burant and S. S. Iyengar and J. Tomasi and M. Cossi and J. M. Millam and M. Klene and C. Adamo and R. Cammi and J. W. Ochterski and R. L. Martin and K. Morokuma and O. Farkas and J. B. Foresman and D. J. Fox},
title={Gaussian~16 {R}evision {A}.03},
year={2016},
note={Gaussian Inc. Wallingford CT}
}

@Article{Thompson_ComputPhysCommun_2022_v271_p108171,
    author =   {Aidan P. Thompson and H. Metin Aktulga and Richard Berger and Dan S.
             Bolintineanu and W. Michael Brown and Paul S. Crozier and Pieter J.
             {in 't Veld} and Axel Kohlmeyer and Stan G. Moore and Trung Dac Nguyen
             and Ray Shan and Mark J. Stevens and Julien Tranchida and Christian
             Trott and Steven J. Plimpton},
    title =    {{LAMMPS - a flexible simulation tool for particle-based materials
             modeling at the atomic, meso, and continuum scales}},
    journal =  {Comput. Phys. Commun.},
    year =     2022,
    volume =   271,
    pages =    108171,
    doi =      {10.1016/j.cpc.2021.108171},
}

@Article{Lu_JChemTheoryComput_2022_v18_p5559,
    author =   {Denghui Lu and Wanrun Jiang and Yixiao Chen and Linfeng Zhang and
             Weile Jia and Han Wang and Mohan Chen},
    title =    {{DP Compress: A Model Compression Scheme for Generating Efficient Deep
             Potential Models}},
    journal =  {J. Chem. Theory Comput.},
    year =     2022,
    volume =   18,
    issue =    9,
    pages =    {5559--5567},
    annote =   {Machine-learning-based interatomic potential energy surface (PES)
             models are revolutionizing the field of molecular modeling. However,
             although much faster than electronic structure schemes, these models
             suffer from costly computations via deep neural networks to predict
             the energy and atomic forces, resulting in lower running efficiency as
             compared to the typical empirical force fields. Herein, we report a
             model compression scheme for boosting the performance of the Deep
             Potential (DP) model, a deep learning-based PES model. This scheme, we
             call DP Compress, is an efficient postprocessing step after the
             training of DP models (DP Train). DP Compress combines several DP-
             specific compression techniques, which typically speed up DP-based
             molecular dynamics simulations by an order of magnitude faster and
             consume an order of magnitude less memory. We demonstrate that DP
             Compress is sufficiently accurate by testing a variety of physical
             properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to
             both CPU and GPU machines and is publicly available online.},
    doi =      {10.1021/acs.jctc.2c00102},
}

@Article{Wang_ComputPhysCommun_2018_v228_p178,
    author =   {Han Wang and Linfeng Zhang and Jiequn Han and Weinan E},
    title =    {{DeePMD-kit: A deep learning package for many-body potential energy
             representation and molecular dynamics}},
    journal =  {Comput. Phys. Commun.},
    year =     2018,
    volume =   228,
    pages =    {178--184},
    doi =      {10.1016/j.cpc.2018.03.016},
}

@Article{Zhang_ComputPhysCommun_2020_v253_p107206,
    author =   {Yuzhi Zhang and Haidi Wang and Weijie Chen and Jinzhe Zeng and Linfeng
             Zhang and Han Wang and Weinan E},
    title =    {{DP-GEN: A concurrent learning platform for the generation of reliable
             deep learning based potential energy models}},
    journal =  {Comput. Phys. Commun.},
    year =     2020,
    volume =   253,
    pages =    107206,
    doi =      {10.1016/j.cpc.2020.107206},
}

@Article{Aktulga_ParallelComputing_2012_v38_p245,
    author =   {H.M. Aktulga and J.C. Fogarty and S.A. Pandit and A.Y. Grama},
    title =    {{Parallel reactive molecular dynamics: Numerical methods and
             algorithmic techniques}},
    journal =  {Parallel Computing},
    year =     2012,
    volume =   38,
    issue =    {4-5},
    pages =    {245--259},
    doi =      {10.1016/j.parco.2011.08.005},
}

@Article{O'Boyle_JCheminform_2011_v3_p33,
    author =   {Noel M O'Boyle and Michael Banck and Craig A James and Chris Morley
             and Tim Vandermeersch and Geoffrey R Hutchison},
    title =    {{Open Babel: An open chemical toolbox}},
    journal =  {J. Cheminform.},
    year =     2011,
    volume =   3,
    issue =    1,
    pages =    33,
    annote =   {A frequent problem in computational modeling is the interconversion of
             chemical structures between different formats. While standard
             interchange formats exist (for example, Chemical Markup Language) and
             de facto standards have arisen (for example, SMILES format), the need
             to interconvert formats is a continuing problem due to the multitude
             of different application areas for chemistry data, differences in the
             data stored by different formats (0D versus 3D, for example), and
             competition between software along with a lack of vendor-neutral
             formats.},
    PMCID =    {PMC3198950},
    doi =      {10.1186/1758-2946-3-33},
}

@Article{Kuhne_JChemPhys_2020_v152_p194103,
    author =   {Thomas D K{\"u}hne and Marcella Iannuzzi and Mauro {Del Ben} and
             Vladimir V Rybkin and Patrick Seewald and Frederick Stein and Teodoro
             Laino and Rustam Z Khaliullin and Ole Sch{\"u}tt and Florian
             Schiffmann and Dorothea Golze and Jan Wilhelm and Sergey Chulkov and
             Mohammad Hossein Bani-Hashemian and Val{\'e}ry Weber and Urban
             Bor{\v{s}}tnik and Mathieu Taillefumier and Alice Shoshana Jakobovits
             and Alfio Lazzaro and Hans Pabst and Tiziano M{\"u}ller and Robert
             Schade and Manuel Guidon and Samuel Andermatt and Nico Holmberg and
             Gregory K Schenter and Anna Hehn and Augustin Bussy and Fabian
             Belleflamme and Gloria Tabacchi and Andreas Gl{\"o}{\ss} and Michael
             Lass and Iain Bethune and Christopher J Mundy and Christian Plessl and
             Matt Watkins and Joost VandeVondele and Matthias Krack and J{\"u}rg
             Hutter},
    title =    {{CP2K: An electronic structure and molecular dynamics software package
             - Quickstep: Efficient and accurate electronic structure calculations}},
    journal =  {J. Chem. Phys.},
    year =     2020,
    volume =   152,
    issue =    19,
    pages =    194103,
    annote =   {CP2K is an open source electronic structure and molecular dynamics
             software package to perform atomistic simulations of solid-state,
             liquid, molecular, and biological systems. It is especially aimed at
             massively parallel and linear-scaling electronic structure methods and
             state-of-the-art ab initio molecular dynamics simulations. Excellent
             performance for electronic structure calculations is achieved using
             novel algorithms implemented for modern high-performance computing
             systems. This review revisits the main capabilities of CP2K to perform
             efficient and accurate electronic structure simulations. The emphasis
             is put on density functional theory and multiple post-Hartree-Fock
             methods using the Gaussian and plane wave approach and its augmented
             all-electron extension.},
    doi =      {10.1063/5.0007045},
}

