Molecular dynamics simulations of CaCl2–NaCl molten salt based on the machine learning potentials

Xie, Yun; Bu, Min; Zou, Guiming; Zhang, Ye; Lu, Guanzhong

doi:10.1016/j.solmat.2023.112275

Cited by 11 publications

(1 citation statement)

References 37 publications

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“…图 2 利用 AIMD 和 DPMD 模拟的 MgCl 2 -KCl 超胞 [51] Figure 2 Supercells MgCl 2 -KCl simulated by AIMD and DPMD [51] . Copyright 2021, American Chemical Society [48,[50][51][52] Table 1 The length (Å) of first coordination shell in ACln-BCl molten salts [48,[50][51][52] 体系(ACl n -BCl)…”

Section: 第一性原理分子动力学mentioning

confidence: 99%

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

Han,

Lan,

Liu

et al. 2023

Acta Chimica Sinica

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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.

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