发表机构
Portland State University（波特兰州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出机器学习策略MLLocP，在保留全电子PAW形式的前提下，大幅加速了复杂材料的电子-声子相互作用计算，在多种材料上验证了其精度与效率。
AI 中文摘要
电子-声子相互作用决定了固体中的载流子动力学、输运以及许多光学和量子现象，但对于含N个原子的体系，在投影缀加波（PAW）框架内的有限位移计算需要多达6N次自洽超胞计算，这使得该方法对于大尺寸、低对称性和无序材料的计算成本极高。我们提出了MLLocP，一种机器学习策略，该策略学习实空间网格上的自洽局域势，并将其位移导数提供给全电子PAW电子-声子矩阵元的计算。对PAW分解的分析表明，局域势贡献是机器学习的天然目标，而其余PAW量则保留在已建立的VASP/PHELEL框架内。我们使用目标网格点采样和特征多样性选择进行高效模型训练，在元素Cu、极性BAs和化学无序Cu3Au上验证了MLLocP。学习到的势及其产生的位移导数与直接密度泛函理论计算紧密吻合，对于BAs迁移率，输运系数误差约为3.5%；对于Cu电导率，误差约为8%；对于Cu3Au电导率，误差约为11%。对于含32个原子的Cu3Au特殊准随机结构，使用40个采样构型相比直接所需的192个位移结构，减少了约五倍的自洽计算次数；使用20或10个构型时，加速比分别提高到约10倍和20倍，对应的电导率误差分别为14.3%和18.6%。因此，MLLocP为复杂材料的PAW电子-声子计算提供了可扩展的途径，同时保留了底层的全电子形式。
英文摘要
Electron-phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the established VASP/PHELEL framework. Using targeted grid-point sampling and feature-diversity selection for efficient model training, we validate MLLocP for elemental Cu, polar BAs, and chemically disordered Cu3Au. The learned potentials and their resulting displacement derivatives closely reproduce direct density functional theory calculations, yielding transport coefficients within approximately 3.5% for BAs mobility, 8% for Cu conductivity, and 11% for Cu3Au conductivity. For the 32-atom Cu3Au special quasirandom structure, using 40 sampled configurations reduces the number of self-consistent calculations by about fivefold relative to the 192 displaced structures required directly; using 20 or 10 configurations increases the acceleration to approximately 10- and 20-fold, with conductivity errors of 14.3% and 18.6%, respectively. MLLocP thus provides a scalable route to PAW electron-phonon calculations in complex materials while preserving the underlying all-electron formalism.
