Atomate2: modular workflows for materials 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.

Keywords:

density functional theory

high-throughput computing

computational materials science

workflow automation

machine learning force fields

Atomate2: modular workflows for materials science

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