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Title: Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

Catalysts for oxygen electrochemical processes are critical for the commercial viability of renewable energy storage and conversion devices such as fuel cells, artificial photosynthesis, and metal-air batteries. Transition metal oxides are an excellent system for developing scalable, non-noble-metal-based catalysts, especially for the oxygen evolution reaction (OER). Central to the rational design of novel catalysts is the development of quantitative structure-activity relation-ships, which correlate the desired catalytic behavior to structural and/or elemental descriptors of materials. The ultimate goal is to use these relationships to guide materials design. In this study, 101 intrinsic OER activities of 51 perovskites were compiled from five studies in literature and additional measurements made for this work. We explored the behavior and performance of 14 descriptors of the metal-oxygen bond strength using a number of statistical approaches, including factor analysis and linear regression models. We found that these descriptors can be classified into five descriptor families and identify electron occupancy and metal-oxygen covalency as the dominant influences on the OER activity. However, multiple descriptors still need to be considered in order to develop strong predictive relationships, largely outperforming the use of only one or two descriptors (as conventionally done in the field). Here, we confirmed that themore » number of d electrons, charge-transfer energy (covalency), and optimality of eg occupancy play the important roles, but found that structural factors such as M-O-M bond angle and tolerance factor are relevant as well. With these tools, we demonstrate how statistical learning can be used to draw novel physical insights and combined with data mining to rapidly screen OER electrocatalysts across a wide chemical space.« less
Authors:
 [1] ;  [2] ;  [3]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science & Engineering
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Sloan School of Management
  3. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials Science & Engineering; Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Mechanical Engineering
Publication Date:
Grant/Contract Number:
SC0002633
Type:
Published Article
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
Journal Volume: 120; Journal Issue: 1; Journal ID: ISSN 1932-7447
Publisher:
American Chemical Society
Research Org:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE
OSTI Identifier:
1374900
Alternate Identifier(s):
OSTI ID: 1435723

Hong, Wesley T., Welsch, Roy E., and Shao-Horn, Yang. Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation. United States: N. p., Web. doi:10.1021/acs.jpcc.5b10071.
Hong, Wesley T., Welsch, Roy E., & Shao-Horn, Yang. Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation. United States. doi:10.1021/acs.jpcc.5b10071.
Hong, Wesley T., Welsch, Roy E., and Shao-Horn, Yang. 2015. "Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation". United States. doi:10.1021/acs.jpcc.5b10071.
@article{osti_1374900,
title = {Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation},
author = {Hong, Wesley T. and Welsch, Roy E. and Shao-Horn, Yang},
abstractNote = {Catalysts for oxygen electrochemical processes are critical for the commercial viability of renewable energy storage and conversion devices such as fuel cells, artificial photosynthesis, and metal-air batteries. Transition metal oxides are an excellent system for developing scalable, non-noble-metal-based catalysts, especially for the oxygen evolution reaction (OER). Central to the rational design of novel catalysts is the development of quantitative structure-activity relation-ships, which correlate the desired catalytic behavior to structural and/or elemental descriptors of materials. The ultimate goal is to use these relationships to guide materials design. In this study, 101 intrinsic OER activities of 51 perovskites were compiled from five studies in literature and additional measurements made for this work. We explored the behavior and performance of 14 descriptors of the metal-oxygen bond strength using a number of statistical approaches, including factor analysis and linear regression models. We found that these descriptors can be classified into five descriptor families and identify electron occupancy and metal-oxygen covalency as the dominant influences on the OER activity. However, multiple descriptors still need to be considered in order to develop strong predictive relationships, largely outperforming the use of only one or two descriptors (as conventionally done in the field). Here, we confirmed that the number of d electrons, charge-transfer energy (covalency), and optimality of eg occupancy play the important roles, but found that structural factors such as M-O-M bond angle and tolerance factor are relevant as well. With these tools, we demonstrate how statistical learning can be used to draw novel physical insights and combined with data mining to rapidly screen OER electrocatalysts across a wide chemical space.},
doi = {10.1021/acs.jpcc.5b10071},
journal = {Journal of Physical Chemistry. C},
number = 1,
volume = 120,
place = {United States},
year = {2015},
month = {12}
}