从文献中构建有机材料数据库的AI驱动的工作流:整合大型语言模型
Hang Hu1, Henry J Stirrat2, Adam Alayli3
1Molecular Engineering & Science Institute, University of Washington, Seattle, Washington 98195, United States.
ACS omega
|November 3, 2025
概括
我们使用机器学习 (ML) 和大语言模型 (LLM) 来自动化材料科学数据库构建. 这通过有效地从出版物中提取数据来加速研究,克服了手册限制.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 数据科学数据科学数据科学
背景情况:
- 材料科学数据库建设传统上是手工,耗时,劳动密集型的.
- 自动化这一过程对于加速科学发现和实现数据驱动研究至关重要.
- 现有的方法缺乏有效性和可扩展性,无法处理日益增长的科学文献量.
研究的目的:
- 开发和验证一个端到端的自动化工作流程,从已发表的文献中构建材料科学数据库.
- 系统地评估和比较不同的机器学习 (ML) 方法,以优化工作流程的每个阶段.
- 通过整合AI/ML进行化学结构图像分析,增强工作流程对有机材料的适用性.
主要方法:
- 利用基于大型语言模型 (LLM) 的嵌入,集群和直接的LLM查询来识别相关出版物.
- 采用OpenAI的GPT-4来准确地提取材料及其属性的数据.
- 从化学结构图像中自动生成SMILES的集成AI/ML方法.
主要成果:
- 使用LLM嵌入,集群和LLM查询的组合实现了出版物识别准确性.
- 证明了GPT-4的数据提取准确度与手动策划相美.
- 成功地将工作流应用于有机光伏设备中的有机捐赠材料,验证了其效率和准确性.
结论:
- 开发的自动化工作流显著加速了材料科学数据库的构建.
- 这些发现为选择最佳的ML方法提供了建议,用于数据库构建中的特定任务.
- 这一进步使数据科学在材料研究中的应用成为可能,而此前数据可用性受到限制.
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