Matsumoto Kentaro, Misawa Nana, Kanesato Shuhei, Nagaoka Masataka
Graduate School of Informatics, Nagoya University, Nagoya, Japan.
Institute of Innovation for Future Society, Nagoya University, Nagoya, Japan.
Front Chem. 2025 Jun 10;13:1618025. doi: 10.3389/fchem.2025.1618025. eCollection 2025.
Understanding the microscopic catalytic mechanism of the olefin polymerization reaction is crucial for the rational design of next-generation catalysts. However, the dynamic nature of the active species, including the fluctuations of the ion pair structure and the orientation of substituents, presents significant challenges for theoretical approaches. In this paper, we present an overview of our recent computational studies on the role of the structural dynamics of the active species of olefin polymerization catalyst in determining reactivity, especially focusing on a novel olefin polymerization catalyst (pyridylamido) Hf(IV) complex. Utilizing the molecular dynamics method and our Red Moon method, a novel methodology we have developed for atomistic simulation of complex chemical reaction systems, we elucidate how the dynamic features, including anion coordination and steric interaction, govern the reactivity in key steps such as ligand modification and propagation reactions. In addition, we demonstrate how machine learning techniques can be applied to extract chemically meaningful descriptors from the structural ensemble obtained from atomistic simulation data of complex chemical reaction systems, thereby identifying the substituents that play an important role in propagation reactions. Our studies highlight the importance of incorporating molecular-level dynamic features of catalysts into mechanistic models.
了解烯烃聚合反应的微观催化机理对于下一代催化剂的合理设计至关重要。然而,活性物种的动态性质,包括离子对结构的波动和取代基的取向,给理论方法带来了重大挑战。在本文中,我们概述了我们最近关于烯烃聚合催化剂活性物种的结构动力学在决定反应性方面作用的计算研究,特别关注一种新型烯烃聚合催化剂(吡啶基酰胺)Hf(IV)配合物。利用分子动力学方法和我们开发的用于复杂化学反应系统原子模拟的新方法——红月方法,我们阐明了包括阴离子配位和空间相互作用在内的动态特征如何在配体修饰和链增长反应等关键步骤中控制反应性。此外,我们展示了如何应用机器学习技术从复杂化学反应系统的原子模拟数据获得的结构系综中提取具有化学意义的描述符,从而识别在链增长反应中起重要作用的取代基。我们的研究强调了将催化剂的分子水平动态特征纳入机理模型的重要性。