{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Other/Nonlisted Topic","url":"https://skillfed.io/packages/category/other-nonlisted-topic"}],"enrichment":{"capability":"Atomate2 provides a library of pre-built materials science workflows that automate complex computational tasks like band structure calculations, elastic properties, phonons, and defect analysis, orchestrated through the jobflow framework.","skillfed_tags":["dft-workflows","high-throughput-materials","vasp-automation"],"use_cases":["Compute band structures and electronic properties for a series of candidate materials in a single Python script.","Generate elastic and piezoelectric tensor data for materials screening without writing individual VASP input files.","Build a searchable database of calculated phonon properties and defect formation energies across hundreds of materials.","Chain multiple calculations (e.g., structure relaxation followed by band structure) with automatic error handling and resubmission.","Scale from testing a workflow on one material to running it across thousands in a distributed computing environment."],"what_it_does":"Atomate2 is a Python library that automates complex materials science computational workflows, primarily for density functional theory (DFT) calculations using VASP. It builds on pymatgen, custodian, and jobflow to provide a collection of pre-configured workflows for computing materials properties\u2014band structures, elastic and dielectric tensors, phonons, defect formation energies, and bonding analysis\u2014while handling job orchestration, error recovery, and result tracking.\n\nWorkflows are composed using Maker objects with a consistent API, allowing users to modify input parameters and chain calculations together. Atomate2 can scale from single-material studies to high-throughput campaigns, automatically maintaining detailed records of jobs, directories, and runtime parameters. Results can be stored in external databases for systematic querying and analysis. Workflows run either locally via jobflow or distributed through jobflow-remote or FireWorks.","worth_installing":"Yes. Atomate2 is actively maintained, production-stable, permissively licensed, and solves a real problem for materials scientists: automating repetitive DFT workflows at scale. Install friction is low and there are no known security vulnerabilities. The main prerequisite is having VASP and its pseudopotentials configured\u2014a non-trivial setup that the documentation addresses, but not a fault of the package itself."},"id":"atomate2","links":{"html":"https://skillfed.io/packages/atomate2","md":"https://skillfed.io/packages/atomate2.md","pypi":"https://pypi.org/project/atomate2/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-13","license_spdx":"BSD-3-Clause-LBNL","license_treatment":"permissive","name":"atomate2","python_support":"supports_current","summary":"atomate2 is a library of materials science workflows"},"popularity":{"monthly_downloads":106625,"position":12645,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.5"}