文字挖掘和MOF合成预测的ChatGPT化学助手
Zhiling Zheng1,2,3, Oufan Zhang, Christian Borgs3,4
1Department of Chemistry, University of California, Berkeley, California 94720, United States.
Journal of the American Chemical Society
|August 7, 2023
概括
快速工程使ChatGPT可靠地从科学文献中提取金属有机框架 (MOF) 合成数据,克服幻觉问题. 这使材料科学研究和聊天机器人开发的数据挖掘自动化.
科学领域:
- 材料科学
- 计算化学
- 化学中的人工智能
背景情况:
- 像ChatGPT这样的大型语言模型在科学应用中面临着幻觉的挑战.
- 自动化从各种科学文献中提取合成条件对于材料发现至关重要.
研究的目的:
- 使用ChatGPT开发一个快速的工程策略 (ChemPrompt Engineering),用于使用MOF合成数据的可靠文本挖掘.
- 为了减轻LLM幻觉,并使有效的数据提取和统一科学文献.
主要方法:
- 开发了一个工作流程,其中包含三个 ChatGPT 编程的文本挖掘过程,用于解析,搜索,过,分类,总结和数据统一.
- 应用ChemPrompt工程策略,从同行评审的文章中提取约800个MOF的26257个合成参数.
主要成果:
- 通过使用开发的策略,在文本挖掘中获得了高精度,回忆和F1分数 (90-99%).
- 构建了一个机器学习模型,准确度高于87%以预测MOF结晶结果.
- 创建了一个基于数据的MOF聊天机器人,用于化学反应和合成查询.
结论:
- ChemPrompt工程方法使MOF合成数据的可靠和自动化文本挖掘成为可能,克服了LLM的限制.
- 提取的数据有助于机器学习预测MOF结晶并识别关键因素.
- 开发的 ChatGPT 化学助理系统在化学子学科中具有广泛的适用性.
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