Cited by 920CO2 Reduction Techniques and CatalystsElectrocatalysts for Energy ConversionMachine Learning in Materials Science

Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

Tran, Kevin, Ulissi, Zachary W.·2018·Nature Catalysis

Integrity signals

Registry

Crossref · 10.1038/s41929-018-0142-1

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Tran, K., & Ulissi, Z. W.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Ulissi, Zachary W.
Venue
Nature Catalysis
Published
2018-09-12
Volume
1
Issue
9
Pages
696-703
ISSN
2520-1158 (ISSN-L)
OpenAlex
W2890961624
Cited by
920
References
48
Citations by year
2026: 143 · 2025: 158 · 2024: 133 · 2023: 118 · 2022: 126 · 2021: 96 · 2020: 91 · 2019: 51 · 2018: 4
FWCI
13.52
Type
journal article
Language
en
Open access
closed
Locations
Nature Catalysis (publishedVersion)
Funders
  • U.S. Department of Energy
  • National Energy Research Scientific Computing Center
  • Office of Science
Topics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
electrocatalysts, CO2 reduction, hydrogen evolution reaction, intermetallic alloys, machine learning, density functional theory, electrochemical energy storage, active learning (machine learning)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review