KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 27 Sep 2024.
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28 of 57 references.
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