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

@article{Cao_PhysChemChemPhys_2022_v24_p11801,
  title        = {
    {Ab initio neural network MD simulation of thermal decomposition of a high
    energy material CL-20/TNT}
  },
  author       = {Liqun Cao and Jinzhe Zeng and Bo Wang and Tong Zhu and John Z H Zhang},
  year         = 2022,
  journal      = {Phys. Chem. Chem. Phys.},
  volume       = 24,
  pages        = {11801--11811},
  doi          = {10.1039/D2CP00710J},
  issue        = 19,
  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},
}

@misc{g16,
  title        = {Gaussian~16 {R}evision {A}.03},
  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
  },
  year         = 2016,
  note         = {Gaussian Inc. Wallingford CT},
}

@article{Kuhne_JChemPhys_2020_v152_p194103,
  title        = {
    {CP2K: An electronic structure and molecular dynamics software package -
    Quickstep: Efficient and accurate electronic structure calculations}
  },
  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
  },
  year         = 2020,
  journal      = {J. Chem. Phys.},
  volume       = 152,
  pages        = 194103,
  doi          = {10.1063/5.0007045},
  issue        = 19,
  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.
  },
}

@article{Lu_JChemTheoryComput_2022_v18_p5559,
  title        = {
    {DP Compress: A Model Compression Scheme for Generating Efficient Deep
    Potential Models}
  },
  author       = {
    Denghui Lu and Wanrun Jiang and Yixiao Chen and Linfeng Zhang and Weile Jia
    and Han Wang and Mohan Chen
  },
  year         = 2022,
  journal      = {J. Chem. Theory Comput.},
  volume       = 18,
  pages        = {5559--5567},
  doi          = {10.1021/acs.jctc.2c00102},
  issue        = 9,
  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.
  },
}

@article{O'Boyle_JCheminform_2011_v3_p33,
  title        = {{Open Babel: An open chemical toolbox}},
  author       = {
    Noel M O'Boyle and Michael Banck and Craig A James and Chris Morley and Tim
    Vandermeersch and Geoffrey R Hutchison
  },
  year         = 2011,
  journal      = {J. Cheminform.},
  volume       = 3,
  pages        = 33,
  doi          = {10.1186/1758-2946-3-33},
  issue        = 1,
  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},
}

@article{Thompson_ComputPhysCommun_2022_v271_p108171,
  title        = {
    {LAMMPS - a flexible simulation tool for particle-based materials modeling
    at the atomic, meso, and continuum scales}
  },
  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
  },
  year         = 2022,
  journal      = {Comput. Phys. Commun.},
  volume       = 271,
  pages        = 108171,
  doi          = {10.1016/j.cpc.2021.108171},
}

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

@incollection{Zeng_2022_Chapter,
  title        = {Chapter 12 - Neural network potentials},
  author       = {Jinzhe Zeng and Liqun Cao and Tong Zhu\textasteriskcentered},
  year         = 2023,
  booktitle    = {Quantum Chemistry in the Age of Machine Learning},
  publisher    = {Elsevier},
  pages        = {279--294},
  doi          = {10.1016/B978-0-323-90049-2.00001-9},
  isbn         = {978-0-323-90049-2},
  editor       = {Pavlo O. Dral},
  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.
  },
}

@article{Zeng_NatCommun_2020_v11_p5713,
  title        = {
    {Complex reaction processes in combustion unraveled by neural network-
    based molecular dynamics simulation}
  },
  author       = {Jinzhe Zeng and Liqun Cao and Mingyuan Xu and Tong Zhu and John Z H Zhang},
  year         = 2020,
  journal      = {Nat. Commun.},
  volume       = 11,
  pages        = 5713,
  doi          = {10.1038/s41467-020-19497-z},
  issue        = 1,
  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},
}

@article{Zeng_PhysChemChemPhys_2020_v22_p683,
  title        = {
    {ReacNetGenerator: an automatic reaction network generator for reactive
    molecular dynamics simulations}
  },
  author       = {
    Jinzhe Zeng and Liqun Cao and Chih-Hao Chin and Haisheng Ren and John Z. H.
    Zhang and Tong Zhu
  },
  year         = 2020,
  journal      = {Phys. Chem. Chem. Phys.},
  volume       = 22,
  number       = 2,
  pages        = {683--691},
  doi          = {10.1039/C9CP05091D},
  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.
  },
}

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