结合机器学习,计算和实验分析,对具有红色到近红外辐射的 (III) 复合体进行分析
Anas Karuth1, Gerardo M Casanola-Martin1, Levi Lystrom2
1Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58108, United States.
The journal of physical chemistry letters
|January 8, 2024
概括
研究人员开发了一种机器学习模型,以预测 (III) 复合物的辐射波长. 这种定量结构与属性关系 (QSPR) 模型有助于设计具有特定光学属性的新复合体.
科学领域:
- 协调化学 协调化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 协调复合物,特别是 (III) 复合物,具有显著的光物理性质.
- 实验和理论研究对于理解和优化这些特性至关重要.
- 预测模型是必要的新型复杂的合理设计.
研究的目的:
- 为了研究单离子 (III) 复合物的光学特性.
- 开发基于机器学习的定量结构-属性关系 (ML/QSPR) 模型,用于预测排放波长.
- 为 (III) 复合体的理论评估和合理设计提供基础.
主要方法:
- 结合实验和计算方法.
- 开发一个解释ML/QSPR模型.
- 分析结构-属性关系,包括二进制连接体中的N-N距离.
主要成果:
- 成功开发了一种可靠的ML/QSPR模型,预测了排放波长.
- 高R平方值 (0.84用于训练,0.87用于测试) 证明了模型的有效性.
- 确定了N-N连接体距离和辐射波长之间的相关性,与几何扭曲和连接体场扰动有关.
结论:
- 开发的ML/QSPR模型有效地预测了 (III) 复合物的光学特性.
- 该研究提供了对影响发射波长的因素的见解,例如几何扭曲.
- 该方法具有设计新 (III) 复合物的潜力,并且可以扩展到其他过渡金属复合物.
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