使用DFT和图形神经网络的基于金属的乙烯寡合化催化剂的结构-特性关系
Zhudan Chen1, Hao Li1, Xiaowei Xu1
1PetroChina Petrochemical Research Institute, Beijing 102206, China. yangweisheng@petrochina.com.cn.
Dalton transactions (Cambridge, England : 2003)
|February 3, 2025
概括
本研究引入了一种新的策略,通过结合密度函数理论 (DFT) 和3D图形神经网络 (3D GNNs) 来设计乙烯寡合化催化剂. 开发的模型准确地预测了连接体结构如何影响催化剂性能,指导创建新型催化剂.
科学领域:
- 催化剂是一种催化剂.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 乙烯的寡合化对于生产有价值的化学品至关重要.
- 需要对催化剂结构与性质关系进行详细的分子理解.
- 目前用于乙烯寡聚化催化剂的联体设计策略有限.
研究的目的:
- 开发一种用于设计乙烯寡合化催化剂的配体的计算策略.
- 建立催化剂结构及其寡合化特性之间的关系.
- 为了指导基于的新型金属催化剂的合理设计.
主要方法:
- 利用密度函数理论 (DFT) 来计算基于的金属催化剂的性能.
- 采用3D图形神经网络 (3D GNN) 来构建催化剂性能的预测模型.
- 应用了加权删除方法和GNN解释算法来分析结构-属性关系.
主要成果:
- 使用3D GNN模型实现了高预测精度 (R2 = 0.93-0.96) 的寡合化选择性.
- 确定了影响三元化选择性的关键连接体亚结构.
- 通过外部验证集 (R2 = 0.92) 证明了强大的概括能力.
结论:
- 结合DFT和3D GNN的方法为理解和设计乙烯寡合催化剂提供了强大的工具.
- 这一策略使得分子层面的洞察力能够了解结构与属性之间的关系.
- 这些发现为开发更高效和更有选择性的酸盐催化剂提供了指导.
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