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Semantic Scholar Open Access 2024 12 sitasi

CataLM: empowering catalyst design through large language models

Ludi Wang Xueqing Chen Yi Du Yuanchun Zhou Yang Gao +1 lainnya
Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (6)

L

Ludi Wang

X

Xueqing Chen

Y

Yi Du

Y

Yuanchun Zhou

Y

Yang Gao

W

Wenjuan Cui

Format Sitasi

APA MLA BibTeX
Wang, L., Chen, X., Du, Y., Zhou, Y., Gao, Y., Cui, W. (2024). CataLM: empowering catalyst design through large language models. https://doi.org/10.1007/s13042-024-02473-0

Akses Cepat

Lihat di Sumber doi.org/10.1007/s13042-024-02473-0
Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Total Sitasi
12×
Sumber Database
Semantic Scholar
DOI
10.1007/s13042-024-02473-0
Akses
Open Access ✓