Electrocatalysis provides a potential solution to pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.
Skip Nav Destination
,
,
,
,
,
,
CHORUS
Article navigation
21 August 2022
Research Article|
August 17 2022
Screening of bimetallic electrocatalysts for water purification with machine learning Available to Purchase
Special Collection:
2022 JCP Emerging Investigators Special Collection
Richard Tran
;
Richard Tran
(Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Department of Chemical Engineering, Carnegie Mellon University
, Pittsburgh, Pennsylvania 15213, USA
Search for other works by this author on:
Duo Wang;
Duo Wang
(Data curation, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing)
2
Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
Search for other works by this author on:
Ryan Kingsbury
;
Ryan Kingsbury
(Conceptualization, Formal analysis, Validation, Writing – original draft, Writing – review & editing)
2
Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
Search for other works by this author on:
Aini Palizhati;
Aini Palizhati
(Software)
1
Department of Chemical Engineering, Carnegie Mellon University
, Pittsburgh, Pennsylvania 15213, USA
Search for other works by this author on:
Kristin Aslaug Persson;
Kristin Aslaug Persson
(Resources, Supervision)
3
Department of Materials Science and Engineering, University of California Berkeley
, Berkeley, California 94720, USA
4
Molecular Foundry, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
Search for other works by this author on:
Anubhav Jain
;
Anubhav Jain
(Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing)
2
Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
Search for other works by this author on:
Zachary W. Ulissi
Zachary W. Ulissi
a)
(Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing)
1
Department of Chemical Engineering, Carnegie Mellon University
, Pittsburgh, Pennsylvania 15213, USA
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Richard Tran
1
Duo Wang
2
Ryan Kingsbury
2
Aini Palizhati
1
Kristin Aslaug Persson
3,4
Anubhav Jain
2
Zachary W. Ulissi
1,a)
1
Department of Chemical Engineering, Carnegie Mellon University
, Pittsburgh, Pennsylvania 15213, USA
2
Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
3
Department of Materials Science and Engineering, University of California Berkeley
, Berkeley, California 94720, USA
4
Molecular Foundry, Lawrence Berkeley National Laboratory
, Berkeley, California 94720, USA
a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the 2022 JCP Emerging Investigators Special Collection.
J. Chem. Phys. 157, 074102 (2022)
Article history
Received:
March 24 2022
Accepted:
June 14 2022
Citation
Richard Tran, Duo Wang, Ryan Kingsbury, Aini Palizhati, Kristin Aslaug Persson, Anubhav Jain, Zachary W. Ulissi; Screening of bimetallic electrocatalysts for water purification with machine learning. J. Chem. Phys. 21 August 2022; 157 (7): 074102. https://doi.org/10.1063/5.0092948
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, et al.
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
Related Content
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion
Appl. Phys. Rev. (February 2025)
Heusler alloy catalysts for electrochemical CO2 reduction
J. Chem. Phys. (August 2022)
Theoretical screening of transition metal single atoms anchored on γ-graphyne as electrocatalysts for nitrogen reduction reaction
Chin. J. Chem. Phys. (February 2025)
Chemical synthesis and characterization of metal-oxide based electrocatalysts for fuel cell reactions
AIP Conf. Proc. (February 2019)
Tandem supported, high metal-loading, non-PGM electrocatalysts for oxygen reduction reaction
APL Energy (April 2024)