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
N1 - Funding Information:
The authors thank J. Rehr, A. Dozier, Chen Zheng and Chi Chen for their contributions towards the FEFF workflow and C. Toher for the discussions on AGL. This work was intellectually led by the U.S. Department of Energy, Office of Basic Energy Sciences, Early Career Research Program (ECRP), which directly supported A.J and A.F's contributions. K.M. and K.P. are supported by the Data Infrastructure Building Blocks (DIBBS) Local Spectroscopy Data Infrastructure (LSDI) project funded by National Science Foundation (NSF), under Award Number 1640899 and the Joint Center for Energy Storage Research (JCESR) project. J.H.M., J.B.N., T.E.S, and K.P. acknowledge support from the Materials Project Center through Grant No. EDCBEE through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02 05CH11231. Work at the Molecular Foundry (J.B.N. and T.E.S.) was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC0205CH11231. M.A. was supported as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. B.B. and Z.L. are supported by the NSF National Research Trainee Fellowship under grant DGE-1449785. S.D. acknowledges support from the Center for the Next Generation of Materials by Design, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Contract No. DE-AC36-08GO28308 to NREL. H. Tang, I.-H. Chu and S.P. Ong acknowledge support from the NSF, SI2-SSI Program under Award No. 1550423 for the development of the NEB workflow. Computational resources were provided by National Energy Research Supercomputing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Computational resources for NEB workflows were provided by Triton Shared Computing Cluster (TSCC) at the University of California, San Diego, NERSC, and the Extreme Science and Engineering Discovery Environment (XSEDE) supported by NSF under Grant No. ACI-1053575.
Publisher Copyright:
© 2017 Elsevier B.V.
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.
UR - http://www.scopus.com/inward/record.url?scp=85026733777&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026733777&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2017.07.030
DO - 10.1016/j.commatsci.2017.07.030
M3 - Article
AN - SCOPUS:85026733777
SN - 0927-0256
VL - 139
SP - 140
EP - 152
JO - Computational Materials Science
JF - Computational Materials Science
ER -