Evidence map›Paper›PMID 40087307›Full record

ArticleNature communications2025

Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning.

Shisheng Zheng, Xi-Ming Zhang, Heng-Su Liu, Ge-Hao Liang, Si-Wang Zhang, Wentao Zhang, Bingxu Wang, Jingling Yang, Xian'an Jin, Feng Pan and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Article
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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

11 authors.

Shisheng Zheng *College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China. zhengss@xmu.edu.cn.
Xi-Ming Zhang *College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Heng-Su LiuCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Ge-Hao LiangCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Si-Wang ZhangCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Wentao ZhangSchool of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen, China.
Bingxu WangSchool of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen, China.
Jingling YangCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Xian'an JinCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China.
Feng PanSchool of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen, China. panfeng@pkusz.edu.cn.ORCID http://orcid.org/0000-0002-8216-1339
Jian-Feng LiCollege of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, College of Electronic Science and Engineering, College of Physical Science and Technology, Institute of Artificial Intelligence, School of Mathematical Sciences, Xiamen University, Xiamen, China. Li@xmu.edu.cn.ORCID http://orcid.org/0000-0003-1598-6856

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22021001,21925404,T2293692,22361132532National Natural Science Foundation of China (National Science Foundation of China) 22402163
6 · The paper itself

Abstract

Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the challenges in the generation and calculation of a vast array of atomic configurations. Here, we present a framework for the automatic and efficient exploration of active phases. This approach utilizes a topology-based algorithm leveraging persistent homology to systematically sample configurations across diverse coordination environments and material morphologies. Simultaneously, efficient machine learning force fields enable rapid computations. We demonstrate the effectiveness of this framework in two systems: hydrogen absorption in Pd, where hydrogen penetrates subsurface layers and the bulk, inducing a "hex" reconstruction critical for CO

Identifiers

PMID40087307
PMCPMC11909169

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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.