基于从头算的强非谐材料载流子迁移率深度学习预测
Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials
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中文总结 AI 辅助
本研究提出结合深度学习哈密顿模型的AI辅助aiKG框架，以KI为基准，将强非谐材料非微扰输运计算成本降至10%，可高效模拟并准确预测其载流子迁移率等性质。
中文摘要 AI 辅助
从头算方法预测强非谐材料（尤其是超低热导体）中的电荷输运仍是一大挑战。这类系统中，电子-声子相互作用的微扰处理和谐波声子图像常失效，需采用非微扰方法。从头算Kubo-Greenwood（aiKG）形式主义为超越谐波近似的温度依赖载流子输运评估提供了严谨框架，但其实际应用计算成本高昂，因需大型超胞、大量统计采样及外推至零频极限。本研究引入结合深度学习哈密顿模型的人工智能（AI）辅助aiKG框架，该模型可对最多含250个原子的超胞预测子-meV精度的Kohn-Sham哈密顿量，规避了昂贵的迭代自洽场计算，同时在训练数据捕获的效应范围内保留从头算可靠性。以强非谐热绝缘体碘化钾（KI）为基准体系，研究表明该方法可高效模拟大型超胞的电子结构与输运性质，重现温度依赖载流子迁移率、谱函数及有效质量，与基础密度泛函理论结果高度一致，且计算成本降至原来的10%。这些结果表明，AI辅助aiKG框架可使强非谐材料的非微扰输运计算易于处理，为现实模拟及新型功能材料的加速发现开辟了可扩展路径。
英文摘要
Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, perturbative treatments of electron-phonon interactions and the harmonic phonon picture often break down, necessitating non-perturbative approaches. The ab initio Kubo-Greenwood(aiKG) formalism provides a rigorous framework for evaluating temperature-dependent carrier transport beyond the harmonic approximation. Nevertheless, its practical application is computationally demanding because it requires large supercells, extensive statistical sampling, and extrapolation to the zero-frequency limit. In this work, we introduce an artificial-intelligence(AI)-assisted aiKG framework that incorporates the deep-learning Hamiltonian model. By predicting the Kohn-Sham Hamiltonian with sub-meV accuracy for supercells of up to 250 atoms, the model bypasses the costly iterative self-consistent field calculations while retaining first-principles reliability within the scope of effects captured by the training data. Using a strongly anharmonic thermal insulator, potassium iodide(KI) as a benchmark system, we demonstrate that the proposed approach enables efficient simulations of electronic structure and transport properties from a large supercell. The framework reproduces temperature-dependent carrier mobilities, spectral functions, and effective masses in close agreement with the underlying density functional theory while reducing computational cost to 10%. These results suggest that the AI-assisted aiKG framework can make non-perturbative transport calculations tractable for strongly anharmonic materials, opening a scalable route towards realistic simulations and accelerated discovery of new functional materials.
