标题： 通过机器学习辅助提取非谐力常数将声子热导率预测加速一个数量级 显示英文标题

标题： Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants

摘要： 从密度泛函理论计算得出材料的声子热导率需要计算昂贵的非简谐原子间力常数，这仍然是高通量材料发现中的计算瓶颈。 在这项工作中，我们提出了一种通过势能曲面局部学习来提取非简谐力常数的机器学习辅助方法。 我们在由220种三元材料组成的多样化集合上演示了我们的方法，这些材料的非简谐力常数评估的总计算时间从480,000个cpu小时减少到少于12,000个cpu小时，同时保持热导率预测精度在10%以内。 我们的方法消除了计算热导率评估的主要障碍，并将推动材料的高通量发现向前发展。 显示英文摘要

摘要： The calculation of material phonon thermal conductivity from density functional theory calculations requires computationally expensive evaluation of anharmonic interatomic force constants and has remained a computational bottleneck in the high-throughput discovery of materials. In this work, we present a machine learning-assisted approach for the extraction of anharmonic force constants through local learning of the potential energy surface. We demonstrate our approach on a diverse collection of 220 ternary materials for which the total computational time for anharmonic force constants evaluation is reduced by more than an order of magnitude from 480,000 cpu-hours to less than 12,000 cpu-hours while preserving the thermal conductivity prediction accuracy to within 10%. Our approach removes a major hurdle in computational thermal conductivity evaluation and will pave the way forward for the high-throughput discovery of materials.