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

Kiran Mathew, Joseph H. Montoya, Alireza Faghaninia, Shyam Dwarakanath, Muratahan Aykol, Hanmei Tang, Iek heng Chu, Tess Smidt, Brandon Bocklund, Matthew Horton, John Dagdelen, Brandon Wood, Zi Kui Liu, Jeffrey Neaton, Shyue Ping Ong, Kristin Persson, Anubhav Jain

Research output: Contribution to journalArticle

41 Citations (Scopus)

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 piezoelectric, dielectric, 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.

Original languageEnglish (US)
Pages (from-to)140-152
Number of pages13
JournalComputational Materials Science
Volume139
DOIs
StatePublished - Nov 2017

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Materials Science
Materials science
materials science
Work Flow
Python
Open Source
pyrotechnics
X ray absorption
automation
Raman Spectra
Ferroelectric materials
Raman scattering
dielectric properties
Band Structure
Energy dissipation
Materials properties
Elastic Properties
templates
Automation
Simulation Analysis

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

Cite this

Mathew, Kiran ; Montoya, Joseph H. ; Faghaninia, Alireza ; Dwarakanath, Shyam ; Aykol, Muratahan ; Tang, Hanmei ; Chu, Iek heng ; Smidt, Tess ; Bocklund, Brandon ; Horton, Matthew ; Dagdelen, John ; Wood, Brandon ; Liu, Zi Kui ; Neaton, Jeffrey ; Ong, Shyue Ping ; Persson, Kristin ; Jain, Anubhav. / Atomate : A high-level interface to generate, execute, and analyze computational materials science workflows. In: Computational Materials Science. 2017 ; Vol. 139. pp. 140-152.
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Mathew, K, Montoya, JH, Faghaninia, A, Dwarakanath, S, Aykol, M, Tang, H, Chu, IH, Smidt, T, Bocklund, B, Horton, M, Dagdelen, J, Wood, B, Liu, ZK, Neaton, J, Ong, SP, Persson, K & Jain, A 2017, 'Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows', Computational Materials Science, vol. 139, pp. 140-152. https://doi.org/10.1016/j.commatsci.2017.07.030

Atomate : A high-level interface to generate, execute, and analyze computational materials science workflows. / Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza; Dwarakanath, Shyam; Aykol, Muratahan; Tang, Hanmei; Chu, Iek heng; Smidt, Tess; Bocklund, Brandon; Horton, Matthew; Dagdelen, John; Wood, Brandon; Liu, Zi Kui; Neaton, Jeffrey; Ong, Shyue Ping; Persson, Kristin; Jain, Anubhav.

In: Computational Materials Science, Vol. 139, 11.2017, p. 140-152.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Atomate

T2 - A high-level interface to generate, execute, and analyze computational materials science workflows

AU - Mathew, Kiran

AU - Montoya, Joseph H.

AU - Faghaninia, Alireza

AU - Dwarakanath, Shyam

AU - Aykol, Muratahan

AU - Tang, Hanmei

AU - Chu, Iek heng

AU - Smidt, Tess

AU - Bocklund, Brandon

AU - Horton, Matthew

AU - Dagdelen, John

AU - Wood, Brandon

AU - Liu, Zi Kui

AU - Neaton, Jeffrey

AU - Ong, Shyue Ping

AU - Persson, Kristin

AU - Jain, Anubhav

PY - 2017/11

Y1 - 2017/11

N2 - 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 piezoelectric, dielectric, 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.

AB - 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 piezoelectric, dielectric, 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.

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JO - Computational Materials Science

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