发表机构
Skolkovo Institute of Science and Technology; HSE University; Digital Materials LLC（斯科尔科沃科学技术研究所; 高等经济学院; 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
通过低秩矩阵和张量分解压缩磁性矩张量势，参数减少1.5-3倍且保持精度，模拟结果与原始模型及DFT和实验一致。
AI 中文摘要
我们提出了一种基于多种矩阵和张量分解的磁性矩张量势（mMTP）参数缩减版本。所得到的压缩磁性机器学习势在不影响验证集预测性能的情况下，将参数数量减少了约1.5至3倍。我们评估了压缩势在Fe-Al和CrN体系的磁性、结构和振动性质以及分子动力学模拟中的表现。我们证明，使用压缩mMTP获得的模拟结果与原始未压缩模型在数值上一致，并与密度泛函理论计算和实验数据相符。
英文摘要
We propose a parameter-reduced version of magnetic Moment Tensor Potential (mMTP) based on various matrix and tensor decompositions. The resulting compressed magnetic machine-learning potential reduces the number of parameters by a factor of approximately 1.5-3 without compromising predictive performance on the validation set. We evaluate the performance of the compressed potentials for magnetic, structural, and vibrational properties, as well as in molecular dynamics simulations of Fe-Al and CrN systems. We demonstrate that the simulation results obtained with the compressed mMTP are numerically consistent with those of the original uncompressed model and are in agreement with density functional theory calculations and experimental data.
Comments11 pages, 11 figures
