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| @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 = {Nature Communications},
year = 2020,
key = {combustion},
volume = 11,
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.},
doi = {10.1038/s41467-020-19497-z},
researchgate = 345758546,
github = {tongzhugroup/mddatasetbuilder},
PMCID = {PMC7658983},
}
@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.},
image = {https://ars.els-cdn.com/content/image/3-s2.0-B9780323900492000019-f12-01-9780323900492.sml},
github = {tongzhugroup/Chapter13-tutorial},
}
@Article{Zeng_JChemTheoryComput_2023_vNone_pNone,
author = {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York},
title = {{QD{\ensuremath{\pi}}: A Quantum Deep Potential Interaction Model for
Drug Discovery}},
journal = {J. Chem. Theory Comput.},
year = 2023,
annote = {We report QD{\ensuremath{\pi}}-v1.0 for modeling the internal energy
of drug molecules containing H, C, N, and O atoms. The
QD{\ensuremath{\pi}} model is in the form of a quantum
mechanical/machine learning potential correction
(QM/{\ensuremath{\Delta}}-MLP) that uses a fast third-order self-
consistent density-functional tight-binding (DFTB3/3OB) model that is
corrected to a quantitatively high-level of accuracy through a deep-
learning potential (DeepPot-SE). The model has the advantage that it
is able to properly treat electrostatic interactions and handle
changes in charge/protonation states. The model is trained against
reference data computed at the {\ensuremath{\omega}}B97X/6-31G* level
(as in the ANI-1x data set) and compared to several other approximate
semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3,
MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QD{\ensuremath{\pi}}
model is demonstrated to be accurate for a wide range of intra- and
intermolecular interactions (despite its intended use as an internal
energy model) and has shown to perform exceptionally well for relative
protonation/deprotonation energies and tautomers. An example
application to model reactions involved in RNA strand cleavage
catalyzed by protein and nucleic acid enzymes illustrates
QD{\ensuremath{\pi}} has average errors less than 0.5 kcal/mol,
whereas the other models compared have errors over an order of
magnitude greater. Taken together, this makes QD{\ensuremath{\pi}}
highly attractive as a potential force field model for drug discovery.},
doi = {10.1021/acs.jctc.2c01172},
}
|
