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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.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
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.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
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.
The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world.
This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational Methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our eforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.
