机器学习势在含能材料分子模拟中的研究进展
Recent Progress toward Molecular Modeling of Energetic Materials by Using Machine Learning Potential
查看参考文献91篇
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文摘
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归纳了机器学习势模型的发展历程、构造方案和训练集搭建策略。机器学习势模型利用第一性原理计算精度的势能面,通过机器学习算法进行重建,已成功用于含能材料燃烧爆炸的分子模拟研究,包括硝胺类含能材料(RDX、CL-20、ICM-102)、氧化剂(AP)和高能颗粒(Al、B)等,并总结了机器学习势在碳氢燃料燃烧方面的研究进展,展望了机器学习势在含能材料分子模拟中所面临的挑战和未来发展前景。指出以深度势(Deep Potential)模型为代表的机器学习势具有高精度、高效率的特点,充分发挥了基于数据驱动的模型训练策略,在保持第一性原理计算精度的同时,可以实现百万原子的分子模拟,具有广阔的应用潜力。提出未来含能材料机器学习势函数的开发将面临如下挑战:(1)如何对极端条件下复杂反应势面进行充分采样;(2)如何提高机器学习势训练集的精度。附参考文献91篇。 |
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其他语种文摘
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A comprehensive review was conducted on the historical development,construction scheme,and training strategy of the machine learning potential.This novel technique approximates the potential energy surface of molecular system at the level of first-principle calculations and has been successfully applied in the molecular modelling of combustion and explosion for energetic materials,including nitramine compounds (RDX,CL-20,and ICM-102),oxidizers (AP) and high-energy particles (Al,B).In addition,the representative applications of machine learning potential on the combustion of hydrocarbon fuels were also introduced.Furthermore,the challenges and future development perspectives of machine learning potential in energetic materials were discussed.It is well demonstrated that machine learning potentials,particularly deep potential models,are highly accurate and efficient.The data-driven approach makes it feasible to empower the simulation of million atoms with a great accuracy as first-principle calculations.In the final remark,the key challenges for the further development of machine learning potentials are discussed:(1) sampling issue for a complex potential energy surface under extreme conditions;(2) accuracy problem in the training dataset.With 91 references. |
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来源
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火炸药学报
,2023,46(5):361-377 【核心库】
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DOI
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10.14077/j.issn.1007-7812.202303015
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关键词
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含能材料
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机器学习势
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第一性原理
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燃烧
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分子动力学
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深度势模型
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地址
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北京理工大学, 爆炸科学与技术国家重点实验室, 北京, 100081
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华东师范大学化学与分子工程学院, 上海市分子治疗与新药开发工程研究中心, 上海, 200062
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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1007-7812 |
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学科
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武器工业;化学工业 |
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基金
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北京理工大学爆炸科学与技术国家重点实验室自主课题
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国家自然科学基金青年项目
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文献收藏号
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CSCD:7488073
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参考文献 共
91
共5页
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|
1.
Badgujar D. Advances in science and technology of modern energetic materials: An overview.
Journal of Hazardous Materials,2008,151(2/3):289-305
|
CSCD被引
133
次
|
|
|
|
|
2.
Gao H X. Azole-based energetic salts.
Chemical Reviews,2011,111(11):7377-7436
|
CSCD被引
132
次
|
|
|
|
|
3.
Zachariah M R. Nanoenergetics: hype,reality and future.
Propellants,Explosives,Pyrotechnics,2013,38(1):7-7
|
CSCD被引
3
次
|
|
|
|
|
4.
王润德. Al基复合含能材料的制备及燃烧性能研究.
火炸药学报,2021,44(6):789-799
|
CSCD被引
3
次
|
|
|
|
|
5.
肖川. 含能材料发展的若干思考.
火炸药学报,2022,45(4):i-iv
|
CSCD被引
2
次
|
|
|
|
|
6.
居学海. 含能材料的量子化学计算与分子动力学模拟综述.
火炸药学报,2012,35(2):1-9
|
CSCD被引
9
次
|
|
|
|
|
7.
严启龙. 含能材料物理化学性能理论预估研究进展.
火炸药学报,2016,39(5):1-12
|
CSCD被引
7
次
|
|
|
|
|
8.
申林. 计算化学中的机器学习.
中国科学:化学,2022,52(6):858-868
|
CSCD被引
6
次
|
|
|
|
|
9.
王涵. 机器学习原子间相互作用建模.
计算数学,2021,43(3):261-278
|
CSCD被引
3
次
|
|
|
|
|
10.
Senftle T P. The reaxff reactive force-field: development,applications and future directions.
NPJ Computational Materials,2016,2(1):1-14
|
CSCD被引
60
次
|
|
|
|
|
11.
Stuart S J. A reactive potential for hydrocarbons with intermolecular interactions.
The Journal of Chemical Physics,2000,112(14):6472-6486
|
CSCD被引
144
次
|
|
|
|
|
12.
Liang T. Variable charge reactive potential for hydrocarbons to simulate organic-copper interactions.
The Journal of Physical Chemistry A,2012,116(30):7976-7991
|
CSCD被引
3
次
|
|
|
|
|
13.
Van Duin A C. Reaxff: a reactive force field for hydrocarbons.
The Journal of Physical Chemistry A,2001,105(41):9396-9409
|
CSCD被引
256
次
|
|
|
|
|
14.
Chenoweth K. Reaxff reactive force field for molecular dynamics simulations of hydrocarbon oxidation.
The Journal of Physical Chemistry A,2008,112(5):1040-1053
|
CSCD被引
98
次
|
|
|
|
|
15.
Yang K. Anisotropic initial reaction mechanism and sensitivity characterization of the layered crystal structure explosive icm-102 under shock loading.
The Journal of Physical Chemistry C,2020,124(19):10367-10375
|
CSCD被引
3
次
|
|
|
|
|
16.
Liu L C. Reaxff-lg: correction of the Reaxff reactive force field for london dispersion, with applications to the equations of state for energetic materials.
The Journal of Physical Chemistry A,2011,115(40):11016-11022
|
CSCD被引
55
次
|
|
|
|
|
17.
Huang X. Effects of different types of defects on ignition mechanisms in shocked p-cyclotetramethylene tetranitramine crystals: a molecular dynamics study based on Reaxff-lg force field.
Journal of Applied Physics,2019,125(19):195101.1-195101.11
|
CSCD被引
1
次
|
|
|
|
|
18.
Chen W. Effects of temperature and wax binder on thermal conductivity of rdx: a molecular dynamics study.
Computational Materials Science,2020,179:109698
|
CSCD被引
1
次
|
|
|
|
|
19.
Chang X Y. Shock-induced anisotropic metal combustion.
The Journal of Physical Chemistry C,2020,124(24):13206-13214
|
CSCD被引
3
次
|
|
|
|
|
20.
Chu Q Z. Ignition and oxidation of core-shell Al/Al_2O_3 nanoparticles in an oxygen atmosphere: insights from molecular dynamics simulation.
The Journal of Physical Chemistry C,2018,122(51):29620-29627
|
CSCD被引
10
次
|
|
|
|
|
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