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Title: The Electrolyte Genome project: A big data approach in battery materials discovery

Abstract

We present a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. The infrastructure is largely open-source and handles both practical aspects (input file generation, output file parsing, and information management) as well as more complex problems (structure matching, salt complex generation, and failure recovery). Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 4830 molecules relevant to battery electrolytes (encompassing almost 55,000 quantum mechanics calculations) at the B3LYP/6-31+G* level. We describe automated workflows for computing redox potential, dissociation constant, and salt-molecule binding complex structure generation. We present routines for automatic recovery from calculation errors, which brings the failure rate from 9.2% to 0.8% for the QChem DFT code. Automated algorithms to check duplication between two arbitrary molecules and structures are described. We present benchmark data on basis sets and functionals on the G2-97 test set; one finding is that a IP/EA calculation method that combines PBE geometry optimization and B3LYP energy evaluation requires less computational cost and yields nearly identical results as compared to a full B3LYP calculation, and could be suitable for the calculation of large molecules. Our data indicates that among the 8 functionals tested,more » XYGJ-OS and B3LYP are the two best functionals to predict IP/EA with an RMSE of 0.12 and 0.27 eV, respectively. Application of our automated workflow to a large set of quinoxaline derivative molecules shows that functional group effect and substitution position effect can be separated for IP/EA of quinoxaline derivatives, and the most sensitive position is different for IP and EA.« less

Authors:
 [1];  [1];  [1];  [2];  [3];  [4];  [1];  [3];  [2];  [1]
+ Show Author Affiliations
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Univ. of Notre Dame, IN (United States)
  4. Univ. of California, San Diego, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1512200
Alternate Identifier(s):
OSTI ID: 1251862
Grant/Contract Number:  
AC02-05CH11231; AC02-06CH11357; EDCBEE
Resource Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 103; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; high-throughput; battery; DFT; ionization potential; electron affinity; IP/EA; dissociation constants; electrolyte

Citation Formats

Qu, Xiaohui, Jain, Anubhav, Rajput, Nav Nidhi, Cheng, Lei, Zhang, Yong, Ong, Shyue Ping, Brafman, Miriam, Maginn, Edward, Curtiss, Larry A., and Persson, Kristin A. The Electrolyte Genome project: A big data approach in battery materials discovery. United States: N. p., 2015. Web. doi:10.1016/j.commatsci.2015.02.050.
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Qu, Xiaohui, Jain, Anubhav, Rajput, Nav Nidhi, Cheng, Lei, Zhang, Yong, Ong, Shyue Ping, Brafman, Miriam, Maginn, Edward, Curtiss, Larry A., & Persson, Kristin A. The Electrolyte Genome project: A big data approach in battery materials discovery. United States. https://doi.org/10.1016/j.commatsci.2015.02.050
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Qu, Xiaohui, Jain, Anubhav, Rajput, Nav Nidhi, Cheng, Lei, Zhang, Yong, Ong, Shyue Ping, Brafman, Miriam, Maginn, Edward, Curtiss, Larry A., and Persson, Kristin A. Wed . "The Electrolyte Genome project: A big data approach in battery materials discovery". United States. https://doi.org/10.1016/j.commatsci.2015.02.050. https://www.osti.gov/servlets/purl/1512200.
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@article{osti_1512200,
title = {The Electrolyte Genome project: A big data approach in battery materials discovery},
author = {Qu, Xiaohui and Jain, Anubhav and Rajput, Nav Nidhi and Cheng, Lei and Zhang, Yong and Ong, Shyue Ping and Brafman, Miriam and Maginn, Edward and Curtiss, Larry A. and Persson, Kristin A.},
abstractNote = {We present a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. The infrastructure is largely open-source and handles both practical aspects (input file generation, output file parsing, and information management) as well as more complex problems (structure matching, salt complex generation, and failure recovery). Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 4830 molecules relevant to battery electrolytes (encompassing almost 55,000 quantum mechanics calculations) at the B3LYP/6-31+G* level. We describe automated workflows for computing redox potential, dissociation constant, and salt-molecule binding complex structure generation. We present routines for automatic recovery from calculation errors, which brings the failure rate from 9.2% to 0.8% for the QChem DFT code. Automated algorithms to check duplication between two arbitrary molecules and structures are described. We present benchmark data on basis sets and functionals on the G2-97 test set; one finding is that a IP/EA calculation method that combines PBE geometry optimization and B3LYP energy evaluation requires less computational cost and yields nearly identical results as compared to a full B3LYP calculation, and could be suitable for the calculation of large molecules. Our data indicates that among the 8 functionals tested, XYGJ-OS and B3LYP are the two best functionals to predict IP/EA with an RMSE of 0.12 and 0.27 eV, respectively. Application of our automated workflow to a large set of quinoxaline derivative molecules shows that functional group effect and substitution position effect can be separated for IP/EA of quinoxaline derivatives, and the most sensitive position is different for IP and EA.},
doi = {10.1016/j.commatsci.2015.02.050},
journal = {Computational Materials Science},
number = C,
volume = 103,
place = {United States},
year = {Wed Apr 01 00:00:00 EDT 2015},
month = {Wed Apr 01 00:00:00 EDT 2015}
}
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