荧光粉
计算机科学
人工神经网络
图形
人工智能
卷积神经网络
试验装置
工作流程
变压器
兴奋剂
机器学习
材料科学
数据库
光电子学
理论计算机科学
工程类
电气工程
电压
作者
Xiang Zhang,Zhiping Zhou,Ming Chen,Yi‐Yang Sun
标识
DOI:10.1021/acs.jpclett.3c02848
摘要
Two challenges facing machine learning tasks in materials science are data set construction and descriptor design. Graph neural networks circumvent the need for empirical descriptors by encoding geometric information in graphs. Large language models have shown promise for database construction via text extraction. Here, we apply OpenAI’s Generative Pre-trained Transformer 4 (GPT-4) and the Crystal Graph Convolutional Neural Network (CGCNN) to the problem of discovering rare-earth-doped phosphors for solid-state lighting. We used GPT-4 to datamine the chemical formulas and emission wavelengths of 264 Eu2+-doped phosphors from 274 articles. A CGCNN model was trained on the acquired data set, achieving a test R2 of 0.77. Using this model, we predicted the emission wavelengths of over 40 000 inorganic materials. We also used transfer learning to fine-tune a bandgap-predicting CGCNN model for emission wavelength prediction. The workflow requires minimal human supervision and is generalizable to other fields.
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