Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning

doi:10.6023/A23070328

Acta Chimica Sinica ›› 2023, Vol. 81 ›› Issue (11): 1663-1672.DOI: 10.6023/A23070328 Previous Articles

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基于机器学习势函数的熔盐体系分子动力学研究进展

韩逸之^a^,^b, 蓝建慧^b^,^*(), 刘学^a^,^*(), 石伟群^b^,^*()

a 西安交通大学金属材料强度国家重点实验室西安 710049
b 中国科学院高能物理研究所北京 100049

投稿日期:2023-07-11 发布日期:2023-08-25

作者简介:

韩逸之, 西安交通大学硕士研究生, 目前研究方向: 机器学习分子动力学.

蓝建慧, 副研究员, 2010年7月博士毕业于北京化工大学化学工程学院, 在中国科学院高能物理研究所工作至今. 长期致力于先进功能材料与有机萃取配体的分子设计, 主要从事核燃料循环化学以及能源环境领域中气体存储分离相关的基础研究. 在量子化学、第一性原理计算、分子动力学以及蒙特卡洛模拟等领域积累了丰富的研究经验, 擅长使用多尺度的计算模拟手段解决复杂的物理化学问题. 迄今为止, 在Angew. Chem. Int. Ed., Nature Commun., ACS Nano, Adv. Funct. Mater., ACS Appl. Mater. Interfaces, Coord. Chem. Rev., J. Electrochem. Soc., Chem. Commun., Inorg. Chem.等国内外知名期刊上发表论文150余篇, 先后主持过多项国家自然科学基金项目.

刘学, 本科毕业于天津大学化工学院, 博士毕业于荷兰莱顿大学, 后在莱顿大学从事博士后研究工作, 2020年加入西安交通大学材料学院. 长期从事框架及碳基新型分离膜材料开发工作. 以第一或通讯作者在Nature Nanotechnology, Nature Communications, ACS Applied Materials & Interfaces等期刊发表论文多篇, 主持国家自然科学基金、陕西省科技厅项目等, 兼职中国核学会锕系物理与化学分会理事.

石伟群, 中国科学院高能物理研究所研究员, 国家杰出青年科学基金获得者, 长期致力于核燃料循环化学与锕系元素化学相关基础研究, 在 JACS, Angew. Chem., Chem, CCS Chem., Nat. Commun., Adv. Mater., Environ. Sci. Technol.等国际知名期刊发表SCI论文300余篇, 成果被国内外同行广泛关注和引用. 文章总引13000余次, H因子58 (Google Scholar). 分别担任英文期刊Journal of Nuclear Fuel Cycle and Waste Tech- nology和Journal of Nuclear Science and Technology的编委与国际顾问编委, 中文期刊《核化学与放射化学》编委. 现为中国化学会核化学与放射化学专业委员会委员、中国核学会锕系物理与化学分会副理事长、中国有色金属学会熔盐化学与技术专业委员会副主任委员、中国核学会核化工分会常务理事.

基金资助:
国家杰出青年科学基金(21925603)

Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning

Yizhi Han^a^,^b, Jianhui Lan^b(), Xue Liu^a(), Weiqun Shi^b()

a State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049
b Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049

Received:2023-07-11 Published:2023-08-25
Contact: *E-mail: lanjh@ihep.ac.cn; liuxue@xjtu.edu.cn; shiwq@ihep.ac.cn
Supported by:
National Science Fund for Distinguished Young Scholars(21925603)

Molten salt is a kind of molten material with important application value. However, the relationship between microstructure and macroscopic properties of molten salt has not been fully investigated, so it is of great significance to carry out extensive molecular dynamics studies. For high-temperature molten salts, molecular dynamics studies mainly relied on the development of force fields in classical molecular dynamics and first principles molecular dynamics previously. Due to the accelerated development of machine learning and neural networks, the potential for molten salts based on machine learning has been developed recently, and great progress has been made in exploring the coordination chemistry and accurately predicting physical properties. Herein, the latest research progress of molecular dynamics related to molten salt was firstly reviewed, especially the development status of machine learning potential. Secondly, the application progress of machine learning potentials in the research of molten salt was summarized. Finally, the research prospect of machine learning potentials for molten salt was discussed, and some suggestions were given.

Key words: molten salt, machine learning, machine learning potential, molecular dynamics, coordination chemistry

Cite this article

Yizhi Han, Jianhui Lan, Xue Liu, Weiqun Shi. Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning[J]. Acta Chimica Sinica, 2023, 81(11): 1663-1672.

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Fig. & Tab. 7

Figure 1. Development procedure of machine learning potentials

体系(ACl_n-BCl)	物质的量比率	T/K	R_A-Cl	R_B-Cl	R_A-A	R_B-B	R_Cl-Cl
MgCl₂-KCl^[51]	8∶17	773	2.379	3.111	3.843	4.697	3.721
	—	873	2.411	3.152	3.894	4.759	3.770
	—	973	2.307	3.110	3.982	4.800	3.803
	—	1073	2.335	3.092	3.976	4.860	3.850
CaCl₂-KCl^[50]	1∶4	1100	2.71	3.12	—	—	3.92
	2∶3	1110	2.62	3.09	—	—	3.79
	1∶1	1058	2.75	3.09	—	—	3.76
	3∶2	1100	2.67	3.12	—	—	3.69
	4∶1	1100	2.66	3.12	—	—	3.57
AlCl₃-LiCl^[52]	1∶1	473	2.171	2.406	—	—	3.579
	—	573	2.197	2.435	—	—	3.503
	—	673	2.108	2.349	—	—	3.554
	—	773	2.141	2.385	—	—	3.486
	—	873	2.175	2.299	—	—	3.542
CaCl₂-NaCl^[48]	4∶1	1100	2.71	2.71	—	—	3.86
	3∶2	—	2.71	2.71	—	—	3.86
	2∶3	—	2.69	2.69	—	—	3.72
	1∶4	—	2.65	2.65	—	—	3.71

Table 1. The length (?) of first coordination shell in ACln-BCl molten salts[48,50?-52]

Figure 4. Free energy profile highlighting the distributions of fluoride coordination number of Zr4+ (left) and zirconium coordination number of F– (right) from different MD simulations[64]. Copyright 2022, American Chemical Society

Figure 6. Workflow diagram of active learning potential

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基于机器学习势函数的熔盐体系分子动力学研究进展

Advances in Molecular Dynamics Studies of Molten Salts Based on Machine Learning

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