Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

TitleAtomate: A high-level interface to generate, execute, and analyze computational materials science workflows
Publication TypeJournal Article
Year of Publication2017
AuthorsKiran Mathew, Joseph H Montoya, Alireza Faghaninia, Shyam S Dwaraknath, Muratahan Aykol, Hanmei Tang, Iek-heng Chu, Tess Smidt, Brandon Bocklund, Matthew Horton, John Dagdelen, Brandon Wood, Zi-Kui Liu, Jeffrey B Neaton, Shyue Ping Ong, Kristin A Persson, Anubhav Jain
JournalComputational Materials Science
Volume139
Pagination140 - 152
Date Published08/2017
ISSN09270256
Abstract

We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility. Built on top of open source Python packages already in use by the materials community such as pymatgen, FireWorks, and custodian, atomate provides well-tested workflow templates to compute various materials properties such as electronic bandstructure, elastic properties, and piezoelectricdielectric, and ferroelectric properties. Atomate also enables the computational characterization of materials by providing workflows that calculate X-ray absorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate is that it provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools. Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations.

DOI10.1016/j.commatsci.2017.07.030
Short TitleComputational Materials Science
Refereed DesignationRefereed