使用人工化学智能预测异质复合体的发射光谱
Yudhajit Pal1, Tahoe A Fiala2, Wesley B Swords2
1Theoretical Chemistry Institute and Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, United States.
我们开发了一个深度学习模型来预测 (III) 复合物的辐射光谱. 这种方法加速了对有机发光二极管和太阳能燃料电池的新材料的发现.
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
- 人工智能的人工智能
背景情况:
- 光异体性化 (III) 复合物对于光电子应用至关重要.
- 预测它们的辐射光谱对于设计新材料至关重要.
- 像DFT这样的当前方法在准确性和效率方面存在局限性.
研究的目的:
- 开发一种深度学习模型,用于准确预测 (III) 复合体中的辐射光谱.
- 为了快速发现新的Ir(III) 染色体.
- 改进用于光谱预测的传统计算方法.
主要方法:
- 在深度学习架构中利用图形神经网络和化学特征.
- 训练模型的实验排放数据的异体质[Ir(ligand) 2(ligand) ]+复合体.
- 嵌入了对杂或低质量的光谱数据的强度.
主要成果:
- 深度学习模型准确地预测了全部的辐射光谱,超过了DFT和波函数方法.
- 模型证明了对不完美的训练数据的稳定性.
- 成功地应用了模型,用于在形预测量身定制的排放特性.
结论:
- 深度学习为预测 (III) 复合物的辐射光谱提供了一个强大的工具.
- 这种方法加速了材料的发现,并减少了合成的努力.
- 模型有助于"设计实验"的战略,以高效的高通量选.
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