洞察循环金属化 (III) 复合物的发光量产量:密度函数理论和机器学习方法
Miho Hatanaka1, Hiromoto Kato1, Minami Sakai2
1Department of Chemistry, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.
The journal of physical chemistry. A
|August 31, 2023
概括
研究人员使用密度函数理论 (DFT) 和机器学习 (ML) 来预测 (III) 复合体中的发光量子产量 (LQYs). 他们发现连接体LUMO能量显著影响LQY,有助于光学材料设计.
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
- 摄影化学的使用.
背景情况:
- 循环金属化 (III) 复合物对于有机发光二极管 (OLED) 和光催化剂等光学材料至关重要.
- 预测发光量子产量 (LQYs) 对于高效的材料设计至关重要,但在计算上具有挑战性.
研究的目的:
- 开发精确的机器学习 (ML) 模型,用于预测 (III) 复合物的LQY.
- 通过整合密度函数理论 (DFT) 和ML来识别控制LQY的关键分子因素.
主要方法:
- 使用DFT研究了九种蓝色到绿色发射 (III) 复合物的非辐射衰变路径.
- 与六坐标和五坐标三重结构之间的能量差异相关联的LQY.
- 使用地面状态 DFT 参数来预测 LQYs 的 ML 建模.
主要成果:
- 确定了连接体单键旋转导致五坐标结构作为常见的火途径.
- 建立了LQY和6和5坐标三重状态之间的能量差异之间的相关性.
- 发现连接体的最低未占用分子轨道 (LUMO) 能量是LQY最重要的负预测因素.
结论:
- 该研究成功地整合了DFT和ML,以预测 (III) 复合物的LQY.
- 降低联体LUMO能量稳定了金属到联体电荷转移 (MLCT) 激发状态,对LQY产生负面影响.
- 这种方法加速了基于的新型光学材料的设计.
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