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arXiv 2602.23728cond-mat.mtrl-sci
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kALDo 2.0:基于第一性原理和机器学习势的可扩展热输运

kALDo 2.0: Scalable Thermal Transport from First Principles and Machine Learning Potentials

University of California Davis(加州大学戴维斯分校);PSI Center for Scientific Computing Theory and Data(瑞士保罗谢勒研究所科学计算理论与数据中心);Massachusetts Institute of Technology(麻省理工学院);National Centre for Computational Design and Discovery of Novel Materials (MARVEL)(国家新材料计算设计与发现中心(MARVEL))

Giuseppe Barbalinardo, Zekun Chen, Dylan Folkner, Bohan Li, Nicholas W. Lundgren, Nathaniel Troup, Alfredo Fiorentino, Davide Donadio

AI总结:

kALDo 2.0结合第一性原理和机器学习势能,提供高效计算复杂材料热输运性质的工具。

摘要:

我们介绍了kALDo2.0,这是一个开源的Python包,用于从第一性原理和机器学习的原子间势能计算固体的振动、弹性及热输运性质。基于非谐晶格动力学(ALD)框架,kALDo2.0提供了高效的CPU和GPU加速的玻尔兹曼输运方程(BTE)求解方法,用于晶体和准谐格林-库波方法(QHGK)。QHGK方法将热输运预测扩展到非晶材料,包括玻璃、合金和复杂纳米结构。kALDo2.0引入了对现代机器学习势能(MLPs)的原生集成,使热输运工作流能够结合第一性原理方法的准确性与经典力场的可扩展性。它还支持温度依赖的有效势能工作流,具有灵活的存储后端以支持大规模计算,并提供高级的非谐性量化。该软件无缝对接电子结构代码(Quantum ESPRESSO、VASP)、分子动力学包(LAMMPS)和MLPs(ACE、NEP、MACE、MatterSim、Orb),使热输运研究从0 K到有限温度。kALDo2.0实现了多种BTE求解策略和必要的物理修正,包括同位素散射和极性材料的非分析项。模块化的Python架构和懒惰评估以及多种存储格式(ASCII、NumPy、HDF5)使能够模拟包含多达数万个原子的系统。本文描述了理论框架、实现细节、软件架构以及验证示例,展示了kALDo2.0在研究复杂材料中的能力,包括具有强非谐性的卤化物钙钛矿和需要长程静电修正的极性氧化物。

英文摘要:

We introduce kALDo2.0, an open-source Python package for computing vibrational, elastic, and thermal transport properties of solids from first principles and machine-learned interatomic potentials. Building on the anharmonic lattice dynamics (ALD) framework, kALDo2.0 provides efficient CPU and GPU-accelerated implementations of the Boltzmann transport equation (BTE) for crystals and the quasi-harmonic Green-Kubo (QHGK) method. QHGK extends thermal transport predictions beyond crystals to disordered materials, including glasses, alloys, and complex nanostructures. kALDo2.0 introduces native integration with modern machine-learned potentials (MLPs), enabling thermal transport workflows that combine the accuracy of first-principles methods with the scalability of classical force fields. It also features comprehensive support for temperature-dependent effective potentials workflows, flexible storage backends for large-scale calculations, and advanced quantification of anharmonicity. The software seamlessly interfaces with electronic structure codes (Quantum ESPRESSO, VASP), molecular dynamics packages (LAMMPS), and MLPs (ACE, NEP, MACE, MatterSim, Orb), enabling thermal transport studies from 0 K to finite temperatures. kALDo2.0 implements multiple BTE solution strategies and essential physical corrections, including isotopic scattering and non-analytical terms for polar materials. A modular Python architecture with lazy evaluation and multiple storage formats (ASCII, NumPy, HDF5) enables simulations of systems containing up to tens of thousands of atoms. This paper describes the theoretical framework, implementation details, software architecture, and validation examples demonstrating kALDo2.0's capabilities for studying complex materials, including halide perovskites with strong anharmonicity and polar oxides requiring long-range electrostatic corrections.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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