Cited by 51Catalysts for Methane ReformingCarbon dioxide utilization in catalysisMachine Learning in Materials Science

Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm

Watanabe, Yuhsuke, Umegaki, Tetsuo, Hashimoto, Masahiko, Omata, Kohji, Yamada, Muneyoshi·2004·Catalysis Today

Integrity signals

Registry

Crossref · 10.1016/j.cattod.2004.02.001

Cites

35 of 35 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?

  • Optimization of a single property is feasible with either direct experimentation or physics-based modeling but can often be accelerated with machine learning.

    “Optimization of a single property is feasible with either direct experimentation (117, 118) or physics-based modeling (119) but can often be accelerated with ML (43, 120, 121 ).”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Watanabe, Y., Umegaki, T., Hashimoto, M., Omata, K., & Yamada, M.
Affiliations
Tohoku University (JP)
Corresponding author
Yamada, Muneyoshi
Venue
Catalysis Today
Publisher
Elsevier BV
Published
2004-06
Volume
89
Issue
4
Pages
455-464
ISSN
0920-5861 (ISSN-L)
OpenAlex
W2027439528
Cited by
51
References
35
Citations by year
2024: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2017: 1 · 2016: 2 · 2012: 1
FWCI
1.89
Type
journal article
Language
en
Open access
closed
Locations
Catalysis Today (publishedVersion)
Funders
  • Japan Society for the Promotion of Science
  • Ministry of Education, Culture, Sports, Science and Technology
Topics
  • Physical Sciences › Chemical Engineering › Catalysis › Catalysts for Methane Reforming
  • Physical Sciences › Chemical Engineering › Process Chemistry and Technology › Carbon dioxide utilization in catalysis
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
methanol synthesis, syngas, high-throughput screening, 96-well plate, artificial neural networks, genetic algorithm, catalyst composition optimization, calcination temperature

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