mlsanityData to 7 Oct 2026

CataLM: Empowering Catalyst Design Through Large Language Models

arXiv 2405.17440 ↗First posted 13 May 2024cs.LG cs.AI cs.CLcited by 3

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Abstract (arXiv)

The field of catalysis holds paramount importance in shaping the trajectory of sustainable development, prompting intensive research efforts to leverage artificial intelligence (AI) in catalyst design. Presently, the fine-tuning of open-source large language models (LLMs) has yielded significant breakthroughs across various domains such as biology and healthcare. Drawing inspiration from these advancements, we introduce CataLM Cata}lytic Language Model), a large language model tailored to the domain of electrocatalytic materials. Our findings demonstrate that CataLM exhibits remarkable potential for facilitating human-AI collaboration in catalyst knowledge exploration and design. To the best of our knowledge, CataLM stands as the pioneering LLM dedicated to the catalyst domain, offering novel avenues for catalyst discovery and development.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

19 of 45 references.

References · 19

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7 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulationscited by 8
  2. 2025AceWGS: An LLM-Aided Framework to Accelerate Catalyst Design for Water-Gas Shift Reactionscited by 1
  3. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0