Treffer: Large 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. Automated exploration of reaction pathways is crucial for advancing reaction mechanisms and catalyst design. Here, the authors introduce ARplorer, a Python and Fortran-based program that integrates quantum mechanics and rule-based methods, underpinned by a large language model-assisted chemical logic, enhancing efficiency in automating searches of multi-step reactions. [ABSTRACT FROM AUTHOR]

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