Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore.
Molecules. 2023 Feb 10;28(4):1715. doi: 10.3390/molecules28041715.
Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
通过有机催化和相转移催化的视角,我们将通过计算化学来检查计算或预测催化性能指标(如转化率和对映选择性的度量)的关键组成部分。本文将通过文献中的示例讨论可用的计算势能的最先进工具,以及相应的局限性,以及自由能。通过有机催化和相转移催化的各种示例,我们将重点介绍与机制、过渡态理论和溶剂化相关的挑战,这些挑战涉及将计算得到的能垒转化为转化率或对映选择性度量。将展示文献中验证其理论模型的示例。最后,将讨论机器学习的相关性和机会。