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.

翻译：近年来，大型语言模型（LLMs）的进展已展现出实现领域特定智能的强大潜力。在本工作中，我们提出了构建一个由人工智能驱动的化学大脑的愿景，该愿景围绕四个核心能力构建化学智能：信息提取、语义解析、基于知识的问答以及推理与规划。我们认为，领域知识和逻辑是使此类系统能够辅助并加速科学发现的重要支柱。为启动这一努力，我们推出了第一代面向化学领域的大型语言模型：KALE-LM-Chem 与 KALE-LM-Chem-1.5，它们在化学相关任务中取得了卓越的性能。我们希望我们的工作能作为一个坚实的起点，助力实现更智能的人工智能，并推动人类科学技术进步与社会发展。