Evidence map›Paper›PMID 40849562›Full record

ArticleCommunications chemistry2025

Large language model guided automated reaction pathway exploration.

Ruzhao Chen, Yubang Liu, Zhe Chen, Yinwu Li, Fuyi Yang, Jiaxin Lin, Zhuofeng Ke

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  2. Article
  3. Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ruzhao ChenSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Yubang LiuSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Zhe ChenSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Yinwu LiSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Fuyi YangSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Jiaxin LinSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China.
Zhuofeng KeSchool of Materials Science and Engineering, PCFM Lab, the Key Laboratory of Low-carbon Chemistry & Energy Conservation of Guangdong Province, Sun Yat-sen University, Guangzhou, P. R. China. kezhf3@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-9064-8051

Funding

National Natural Science Foundation of China (National Science Foundation of China) Nos. 22373118 and 22231002
6 · The paper itself

Abstract

Fast and efficient automated exploration of reaction pathways is essential for studying reaction mechanisms and advancing data-driven approaches for reaction development and catalyst design. Here, we present a new program (utilizing Python and Fortran), capable of conducting automated, fast, and efficient exploration of reaction pathways for potential energy surfaces (PES) studies. This program integrates quantum mechanics and rule-based methodologies, underpinned by a Large Language Model-assisted chemical logic. Both active-learning methods in transition states sampling and parallel multi-step reaction searches with efficient filtering help enhance efficiency and accelerate PES searching. Its effectiveness and versatility in automating searches are exemplified through case studies of multi-step reactions, including the organic cycloaddition reaction, asymmetric Mannich-type reaction, and organometallic Pt-catalyzed reaction. ARplorer's capability to scale up for high-throughput screening significantly enhances its utility, positioning it as an efficient tool for data-driven reaction development and catalyst design.

Identifiers

PMID40849562
PMCPMC12375046

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Registered trials

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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.