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Related Experiment Video

Updated: Sep 10, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Large language model guided automated reaction pathway exploration.

Ruzhao Chen¹, Yubang Liu¹, Zhe Chen¹

¹School 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.

Communications Chemistry

|August 23, 2025

Summary

This summary is machine-generated.

We developed ARplorer, a new program for fast, automated reaction pathway exploration. This tool aids in studying reaction mechanisms and accelerates data-driven catalyst design.

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Area of Science:

Computational Chemistry
Chemical Reaction Dynamics
Catalysis

Background:

Automated exploration of reaction pathways is crucial for understanding reaction mechanisms.
Data-driven approaches require efficient methods for reaction development and catalyst design.

Purpose of the Study:

To present a new program for automated, fast, and efficient exploration of reaction pathways.
To enhance the study of potential energy surfaces (PES) and accelerate reaction discovery.

Main Methods:

Integration of quantum mechanics and rule-based methodologies.
Utilization of Large Language Model-assisted chemical logic.
Implementation of active-learning for transition state sampling and parallel multi-step reaction searches with filtering.

Main Results:

Demonstrated effectiveness and versatility in automating reaction pathway searches.
Successful application to diverse case studies: organic cycloaddition, asymmetric Mannich-type, and Pt-catalyzed reactions.
ARplorer enables high-throughput screening for reaction development and catalyst design.

Conclusions:

ARplorer provides an efficient tool for automated reaction pathway exploration.
The program significantly enhances data-driven reaction development and catalyst design.
Its scalability supports high-throughput screening applications.