biostudies-literatureChen RNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (National Science Foundation of China)255https://www.ebi.ac.uk/biostudies/studies/S-EPMC12375046biostudies-literatureUnknown8(1)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.Communications chemistryLarge language model guided automated reaction pathway exploration.PMC12375046Nos. 22373118 and 22231002Liu YLi YChen ZKe ZLin JChen RYang FfalseLarge language model guided automated reaction pathway exploration.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.2025-01-01T00:00:00Z2025 Aug2026-05-09T17:58:22.959Z2026-04-08T01:08:16.498ZS-EPMC123750464084956210.1038/s42004-025-01630-y