Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Abstract

Microkinetic models of homogeneous catalytic reactions are constructed using ab initio simulations to gain insight into mechanisms, rationalize catalyst performance, and inspire catalyst design.Using a data-driven approach, the classical linear free-energy relationships are extended to multiple linear regression to study both reactivity and selectivity of homogeneous catalysts and build models to rationalize important interactions.Volcano plots are demonstrated as a general analysis framework when comparing potential energy surfaces of related catalytic reactions to extract decisive parameters and visualize breaks in mechanisms.Nonlinear regression models including random forests, support-vector machines, Gaussian processes, and artificial neural networks are applied to predict reaction yields, enantioselectivity, and activation energies of catalytic reactions based on both experimental and computational data.

Uncertainty Quantification of Reactivity Scales**

“…The diversity of benchmarkable reactions can be increased remarkably in this way, which increases the significance of conclusions drawn from theoretical studies and is particularly important in the context of data-driven chemical design. [46][47][48] Currently, we are benchmarking first-principles models of reactivity against results of this study.…”

Melding of Experiment and Theory Illuminates Mechanisms of Metal-Catalyzed Rearrangements: Computational Approaches and Caveats

“…188,189 Others are harnessing the power of machine learning methods for accelerated reaction discovery and chemical space exploration. [190][191][192] Nonetheless, we hope that we have given readers a snapshot of the utility of computational approaches through tales of transition-metalcatalyzed sigmatropic rearrangements. Timely publications describing experimental and computational aspects related to this topic have come out during the preparation of this review and we hope that interested readers will check them out for further reading.…”

Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products

“…They help to identify hidden patterns in data and usually the user aims for a model that generalizes well, meaning it proves good precision in predicting data that was unseen before. [40][41][42] It should be noted that machine-learned models can only be as sophisticated as the data they are fed with. Hence, to unleash the power of machine learning, high-quality data are necessary, which may constitute a bottleneck that requires careful consideration.…”