arrow
Return

CataLM: empowering catalyst design through large language models

delete2025-01-15
delete0
delete
OA
AI
L
Ludi Wang
X
Xueqing Chen
Y
Yi Du
Y
Yuanchun Zhou
Y
Yang Gao *
W
Wenjuan Cui *
DOI:10.1007/s13042-024-02473-0delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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 (Catalytic 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.
Keywords:
AI for science (AI4S)
Large language models (LLMs)
Electrocatalytic materials
Catalyst design

Journal

International Journal of Machine Learning and Cybernetics cover
International Journal of Machine Learning and Cybernetics
IF:
2.7
Papers:
3.1K
Citations:
5.6K

Organization

U
university of chinese academy of sciences, cas
Scholars:
4.1W
Papers: 3.8W
Citations: 75
C
chinese academy of sciences
Scholars:
56.1W
Papers: 44.8W
Citations: 704