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通过图形神经网络进行结构-属性关系的GPT辅助学习:适用于稀土合的应用
Xiang Zhang1, Zichun Zhou1, Chen Ming1
1State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, People's Republic of China.
The journal of physical chemistry letters
|December 8, 2023
概括
这项研究使用大型语言模型和图形神经网络来发现用于照明应用的新光. 由人工智能驱动的方法自动化了数据挖掘和材料预测,加速了材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 固态照明 固态照明
背景情况:
- 材料科学中的机器学习在数据集构建和描述器设计方面面临挑战.
- 图形神经网络 (GNN) 编码材料结构,绕过经验描述器.
- 大型语言模型 (LLM) 显示了从科学文献中自动提取数据的潜力.
研究的目的:
- 使用人工智能发现用于固态照明的稀土合光体.
- 为了自动化数据挖掘和材料属性预测的过程.
- 为加速材料发现开发一种可通用的工作流程.
主要方法:
- OpenAI的生成预训练变压器4 (GPT-4) 用于数据挖掘化学公式和排放波长.
- 一个晶体图卷积神经网络 (CGCNN) 模型被训练在264个Eu2+化上.
- 转移学习被用来微调GNN模型以进行排放波长预测.
主要成果:
- GPT-4成功地从274篇文章中提取了数据,创建了264个的数据集.
- 经过训练的CGCNN模型在辐射波长预测方面获得了0.77的测试R2.
- 超过4万种无机材料被选为潜在的应用.
结论:
- 结合LLM和GNN的使用为材料发现提供了高效,低监督的工作流.
- 这种由人工智能驱动的方法可用于其他材料科学问题和应用.
- 这项研究加速了用于固态照明的新材料的识别.
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