标题： 对机器学习势能函数中力误差对热导率影响的深入理解 显示英文标题

标题： Insight into the effect of force error on the thermal conductivity from machine-learned potentials

摘要： 机器学习势能（MLPs）已被广泛用于通过原子模拟获得晶格热导率。 然而，各种MLPs中的力误差对热传输的影响尚未被广泛认识，并且仍需完全理解。 在此，我们采用MLP驱动的分子动力学（MD）和非谐振晶格动力学（LD）系统地研究了计算的热导率如何随着力误差变化，使用砷化硼作为典型材料。 我们一致观察到，在三种不同的MLPs（包括神经进化势能、深度势能和矩张量势能）的MD模拟中，热导率被低估。 我们提出了一种基于受控力噪声的鲁棒外推方案，通过朗之万恒温器来纠正这种低估。 校正后的结果在200 K至600 K范围内与之前的实验测量结果有良好的一致性。相比之下，使用MLPs进行LD计算得到的热导率值很容易与实验数据一致，这归因于力误差对力常数计算的影响要小得多。 显示英文摘要

摘要： Machine-learned potentials (MLPs) have been extensively used to obtain the lattice thermal conductivity via atomistic simulations. However, the impact of force errors in various MLPs on thermal transport has not been widely recognized and remains to be fully understood. Here, we employ MLP-driven molecular dynamics (MD) and anharmonic lattice dynamics (LD) to systematically investigate how the calculated thermal conductivity varies with the force errors, using boron arsenide as a prototypical material. We consistently observe an underestimation of thermal conductivity in MD simulations with three different MLPs including the neuroevolution potential, deep potential, and moment tensor potential. We provide a robust extrapolation scheme based on controlled force noises via the Langevin thermostat to correct this underestimation. The corrected results achieve a good agreement with previous experimental measurement from 200 K to 600 K. In contrast, the thermal conductivity values from LD calculations with MLPs readily align with the experimental data, which is attributed to the much smaller effects of the force errors on the force-constant calculations.