Evidence map›Paper›PMID 34585115›Full record

ArticleiScience2021

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts.

Haoxin Mai, Tu C Le, Takashi Hisatomi, Dehong Chen, Kazunari Domen, David A Winkler, Rachel A Caruso

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics.Computer methods in applied mechanics and engineering · 2023
    Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Haoxin MaiApplied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
Tu C LeSchool of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
Takashi HisatomiResearch Initiative for Supra-Materials (RISM), Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan.
Dehong ChenApplied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.
Kazunari DomenResearch Initiative for Supra-Materials (RISM), Shinshu University, 4-17-1 Wakasato, Nagano 380-8553, Japan.
David A WinklerMonash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3052, Australia.
Rachel A CarusoApplied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New photocatalysts are traditionally identified through trial-and-error methods. Machine learning has shown considerable promise for improving the efficiency of photocatalyst discovery from a large potential pool. Here, we describe a multi-step, target-driven consensus method using a stacking meta-learning algorithm that robustly predicts bandgaps and H

Indexed as

catalysischemistrycomputational chemistry

Identifiers

PMID34585115
PMCPMC8455646

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.