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High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
Phonons play an essential role in condensed matter physics, influencing key phenomena such as vibrational entropy, thermal conductivity, superconductivity, ferroelectricity, and photoluminescence spectra. Here, we present a comprehensive database of harmonic phonon properties, constructed using automated high-throughput (HT) density functional theory (DFT) calculations for 26,413 compounds. The database covers materials with all seven crystal systems and includes primitive cells with up to 60 atoms. In this work, phonons were computed using DFT-based (PBEsol level of theory) second-order interatomic force constants (IFCs), obtained from perturbed supercell calculations and fitted using either least-squares or LASSO-based regression depending on the number of required finite displacements. The phonon workflow is implemented in the HT software Atomate2, incorporating Pheasy, a compressive sensing lattice dynamics code. Within this framework, space group and point group symmetries, as well as the acoustic sum rule and rotational invariance constraints, are applied to the force constants to ensure physical accuracy and reduce numerical errors. This approach offers a substantial computational speedup compared to both the traditional finitedisplacement method and density functional perturbation theory, while maintaining accuracy comparable to both. The resulting phonon database includes phonon dispersions, phonon density of states (DOS), and derived thermodynamic properties such as Helmholtz free energy (F ), entropy (S), and constant-volume heat capacity (CV). Our work not only establishes an HT methodology for phonon calculations, but also delivers a large-scale phonon database accessible to Materials Project (MP) users for a range of applications, including materials screening and follow-up computational studies.
