发表机构
HSE University; Moscow Engineering Physics Institute; Skolkovo Institute of Science and Technology; Digital Materials LLC（高等经济大学; 莫斯科工程物理学院; 斯科尔科沃科学技术研究所; 数字材料有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过张量列分解实现矩张量势的低秩近似，在1.5倍压缩下使训练集规模减半且精度不变，并验证了两种模型在熔盐和合金性质预测上的一致性。
AI 中文摘要
在本研究中，我们基于张量列（TT）分解实现了矩张量势（MTP）的低秩近似。所实现的张量分解矩张量势（TFMTP）和原始MTP模型通过基于MaxVol的算法，在四组分熔盐混合物LiF-NaF-KF（FLiNaK）的分子动力学模拟以及五组分等原子随机合金MoNbTaWV的几何优化过程中进行主动训练。我们证明，在1.5倍压缩下，TFMTP拟合所需的构型数量比原始MTP模型少两倍，同时保持难以区分的精度水平。这些主动训练的MTP和TFMTP模型进一步用于评估FLiNaK在600至1200 K温度范围内的密度和粘度，以及MoNbTaWV合金在零温度下的弹性常数和体积模量。对于这两种原子系统，MTP和TFMTP预测的物理性质差异可忽略不计。
英文摘要
In this study, we implement a low-rank approximation of Moment Tensor Potential (MTP) based on the tensor train (TT) decomposition. The implemented tensor-factorized MTP (TFMTP) and the original MTP model are actively trained via a MaxVol-based algorithm during molecular dynamics simulations of a four-component molten salt mixture, LiF-NaF-KF (FLiNaK), and geometry optimizations of a five-component equiatomic MoNbTaWV random alloy. We demonstrate that under a 1.5-fold compression, TFMTP requires two times fewer configurations for fitting than the original MTP model, while maintaining an indistinguishable level of accuracy. These actively trained MTP and TFMTP models are further used to evaluate the density and viscosity of FLiNaK at temperatures ranging from 600 to 1200 K, as well as the elastic constants and bulk modulus of the MoNbTaWV alloy at zero temperature. For both atomic systems, the differences in physical properties predicted by MTP and TFMTP are negligible.
