TY - JOUR
T1 - Screening of bimetallic electrocatalysts for water purification with machine learning
AU - Tran, Richard
AU - Wang, Duo
AU - Kingsbury, Ryan
AU - Palizhati, Aini
AU - Persson, Kristin Aslaug
AU - Jain, Anubhav
AU - Ulissi, Zachary W.
N1 - Funding Information:
This work was intellectually led and supported by the National Alliance for Water Innovation (NAWI), funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy Office, Advanced Manufacturing Office under Funding Opportunity Announcement No. DE-FOA-0001905. This research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. Lawrence Berkeley National Laboratory is funded by the Department of Energy under Award No. DE-AC02-05CH11231. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government. The Pourbaix analyses were supported by the Materials Project, which is funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05-CH11231: Materials Project Program No. KC23MP. The authors thank Jin Xun Liu, Samuel D. Young, Nirala Singh, and Bryan R. Goldsmith for fruitful discussions.
Publisher Copyright:
© 2022 Author(s).
PY - 2022/8/21
Y1 - 2022/8/21
N2 - Electrocatalysis provides a potential solution to NO3- 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 NO3- 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.
AB - Electrocatalysis provides a potential solution to NO3- 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 NO3- 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.
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U2 - 10.1063/5.0092948
DO - 10.1063/5.0092948
M3 - Article
C2 - 35987588
AN - SCOPUS:85137008241
SN - 0021-9606
VL - 157
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
IS - 7
M1 - 074102
ER -