Laplaza Ruben, Wodrich Matthew D, Corminboeuf Clemence
Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
J Phys Chem Lett. 2024 Jul 25;15(29):7363-7370. doi: 10.1021/acs.jpclett.4c01657. Epub 2024 Jul 11.
The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if the appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce , a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity.
反应选择性的预测对于计算化学来说是一项具有挑战性的任务，这不仅是因为许多分子具有多种构象，还由于有效活化能与速率常数之间存在指数关系。为了考虑分子的灵活性，越来越多的方法可以生成过渡态（TS）结构的构象集合。通常，这些TS集合采用玻尔兹曼加权，并在假设柯廷-哈米特条件下用于计算选择性。然而，如果不对构象集合进行适当的筛选，这种策略可能会导致错误的预测。在这里，我们展示了如何通过处理模型反应的相同TS集合来获得任何可能的选择性。为了以一致且自动化的方式解决繁重的筛选任务，我们引入了 ，这是一种用于对代表性构象进行模块化分析的工具，有助于避免人为错误，同时将获得正确反应选择性所需的重新优化计算次数降至最低。
