Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent
Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
arXiv:2603.01311 [cs.CL], (1 Mar 2026)
@misc{chandrasekhar2026catalystagent,
title={Catalyst-Agent: Autonomous heterogeneous catalyst screening and optimization with an LLM Agent},
author={Achuth Chandrasekhar and Janghoon Ock and Amir Barati Farimani},
year={2026},
eprint={2603.01311},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.01311}
}
The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for this include time-consuming and expensive experimental trial-and-error approaches in labs based on chemical theory or heavily computational first-principles approaches based on density functional theory. Recent studies show that deep learning models like graph neural networks (GNNs) can significantly speed up the screening and discovery of catalyst materials by many orders of magnitude, with very high accuracy and fidelity.
March 8, 2026 by hgpu
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