Open Access

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.

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

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.