Lam Stephen T, Li Qing-Jie, Ballinger Ronald, Forsberg Charles, Li Ju
Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
ACS Appl Mater Interfaces. 2021 Jun 2;13(21):24582-24592. doi: 10.1021/acsami.1c00604. Epub 2021 May 21.
Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict the structures and dynamics at normal operating conditions, high-temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long time scales (e.g., nanosecond) and large system sizes (e.g., 10 atoms) while maintaining ab initio density functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties.
锂基熔盐因其在储能、先进裂变反应堆和聚变装置中的应用而备受关注。氟化锂，特别是66.6%LiF - 33.3%BeF（氟铍酸钾）在核系统中具有相当大的吸引力，因为它们展现出了良好的传热、中子慢化和嬗变特性的出色组合。对于核盐而言，可能的局部结构、组成和热力学条件范围给原子尺度建模带来了重大挑战。在这项工作中，我们证明了以原子为中心的神经网络原子间势（NNIPs）为熔盐分子动力学提供了一种快速方法，其准确性与从头算分子动力学相当。对于LiF，这些势能够准确再现二聚体的从头算相互作用、变形下的晶体固体、熔点附近的晶体LiF以及高温下的液态LiF。对于氟铍酸钾，NNIPs能准确预测正常运行条件、高温高压条件以及晶体固相下的结构和动力学。此外，我们表明基于NNIP的熔盐分子动力学具有可扩展性，能够达到长时间尺度（例如纳秒）和大系统规模（例如10⁶个原子），同时保持从头算密度泛函理论的准确性，并在计算结构和输运性质时提供超过三个数量级的计算加速。
