κALDo 2.0: Scalable thermal transport from first principles and machine learning potentials
Description
We introduce κALDo 2.0, an open-source Python package for computing vibrational, elastic, and thermal transport properties of crystalline and disordered solids from first principles and machine-learned interatomic potentials. Building on the anharmonic lattice dynamics (ALD) framework, κALDo 2.0 provides efficient CPU and GPU-accelerated implementations of the Boltzmann transport equation (BTE) for crystals and the quasi-harmonic Green-Kubo (QHGK) method. The QHGK formalism extends thermal transport predictions beyond translationally-invariant crystals to materials lacking long-range order, including glasses, alloys, and complex nanostructures. κALDo 2.0 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 (TDEP) 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 state-of-the-art MLPs (ACE, NEP, MACE, MatterSim, Orb), enabling thermal transport studies from 0 K to finite temperatures. κALDo 2.0 implements multiple BTE solution strategies (relaxation time approximation, self-consistent iteration, full matrix inversion, and eigendecomposition) and supports essential physical corrections, including isotopic scattering and non-analytical terms for polar materials. A modular Python architecture with lazy evaluation and multiple storage formats (formatted text, NumPy, HDF5) enables simulations of systems containing more than 10,000 atoms. This paper describes the theoretical framework, implementation details, software architecture, and validation examples demonstrating κALDo 2.0’s capabilities for studying complex materials, including halide perovskites with strong anharmonicity and polar oxides requiring long-range electrostatic corrections.