Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.
@article{arxiv.2409.18695,
title = {KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry},
author = {Weichen Dai and Yezeng Chen and Zijie Dai and Yubo Liu and Zhijie Huang and Yixuan Pan and Baiyang Song and Chengli Zhong and Xinhe Li and Zeyu Wang and Zhuoying Feng and Yi Zhou},
journal= {arXiv preprint arXiv:2409.18695},
year = {2026}
}