预测八极分子的双光子吸收光谱:一种基于分子结构的深度学习方法
Haoqing Fu1, Mengna Zhang1, Jiancai Leng1
1International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
The journal of physical chemistry. A
|January 8, 2024
概括
我们开发了一个快速的深度学习模型来预测八极分子的两光子吸收 (TPA) 光谱. 这种方法显著加快了发现具有强烈TPA反应的先进材料的速度.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 八极分子对于先进的应用至关重要,因为它们具有强大的二光子吸收 (TPA) 特性.
- 预测TPA光谱通常需要计算密集的量子化学计算.
研究的目的:
- 开发一种快速准确的深度学习方法,用于预测八极分子的TPA光谱.
- 为了克服与传统量子化学模拟相关的计算成本.
主要方法:
- 使用了一个图形神经网络 (GNN) 模型.
- 输入分子几何结构作为最初的描述符.
- 预测的最大两光子过渡波长和横截面.
主要成果:
- 与时间依赖密度功能理论 (TD-DFT) 计算相比,平均绝对百分比误差低于4%.
- 深度学习模型准确地重现了实验观察结果.
- 与传统的量子计算相比,演示了近10万倍的速度增加.
结论:
- 开发的深度学习方法为模拟TPA属性提供了一种高效且具有成本效益的工具.
- 这种方法显示了新型TPA材料高通量选的巨大潜力.
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