Evidence map›Paper›PMID 42478896›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts.

Xueyu Hu, Yucun Zhou, Haoyu Li, Zheyu Luo, Nai Shi, Yong Ding, Weining Wang, Weilin Zhang, Doyeub Kim, Chanho Kim and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Xueyu HuSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Yucun ZhouSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Haoyu LiSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Zheyu LuoSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Nai ShiKyushu University Platform of Inter-/Transdisciplinary Energy Research, Kyushu University, Fukuoka, Japan.
Yong DingSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Weining WangSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Weilin ZhangSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Doyeub KimSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Chanho KimSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Yoojin AhnSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Nikhil GovindarajanSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Minda ZouSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Zhijun LiuSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Yuefeng SongSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.
Meilin LiuSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, USA.

Funding

Georgia Tech FoundationU.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell R&D Program DE-EE0011333
6 · The paper itself

Abstract

Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi-physics nature of ORR/OER-together with diverse operating environments-poses significant challenges to the rational design of electrocatalysts based on structure-property relationships. To overcome these challenges, we developed Two-Stage Material Screening (TSMS), an AI-driven framework that integrates density functional theory (DFT) computations, an active-learning-guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts. Demonstrated in protonic solid oxide cells (P-SOCs), TSMS screened 6,940,032 compositions and identified top-performing candidates that were experimentally validated, achieving a peak power density of 2.68 W cm

Indexed as

active learningdensity functional theorymachine learningoxygen electrocatalystsolid oxide cell

Identifiers

PMID42478896
PMCPMC13532494

What OpenQuestion holds

Textmetadata
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.