Screening of bimetallic electrocatalysts for water purification with machine learning

Richard Tran, Duo Wang, Ryan Kingsbury, Aini Palizhati, Kristin Aslaug Persson, Anubhav Jain, Zachary W. Ulissi

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number074102
JournalJournal of Chemical Physics
Volume157
Issue number7
DOIs
StatePublished - Aug 21 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry

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