Automatic Conformational Search of Transition States for Catalytic Reactions Using Genetic Algorithm

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman's rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

Theoretical Perspectives in Organocatalysis

It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.

Automated input structure generation for single-ended reaction path optimizations

The exploration of a reaction network requires highly automated workflows to avoid error-prone and time-consuming manual steps. In this respect, a major bottleneck is the search for transition-state (TS) structures, which frequently fails and, therefore, makes (manual) revision necessary. In this work, we present a technique for obtaining suitable input structures for automated TS searches based on single-ended reaction path optimization algorithms, which makes subsequent TS searches via this method significantly more robust. First, possible input structures are generated based on the spatial alignment of the reactants. The appropriate orientation of reacting groups is achieved via stepwise rotations along selected torsional degrees of freedom. Second, a ranking of the obtained structures is performed according to selected geometric criteria. The main goals are to properly align the reactive atoms, to avoid hindrance within the reaction channel and to resolve steric clashes between the reactants. The developed procedure has been carefully tested on a variety of examples and provides suitable input structures for TS searches within seconds. The method is in daily use in an industrial setting.

Polymerization-Like Mechanism for Fixation of CO2 with Epoxides by Multifunctional Organocatalysts

The commonly accepted mechanism of CO2 fixation of epoxides to cyclic carbonates catalyzed by multifunctional non-halide organocatalysts is challenged by our computational DFT-D3 study, which revealed a new polymerization-like mechanism...

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Bicyclic Guanidine-Catalyzed Asymmetric Cycloaddition Reaction of Anthrones-Bifunctional Binding Modes and Origin of Stereoselectivity

We report a computational analysis of the [5,5] bicyclic guanidine-catalyzed asymmetric cycloaddition reaction of anthrones. Based on extensive conformational search of key intermediates and transition states on the potential energy surface and density functional theory calculations, we studied five plausible binding modes between the guanidine catalyst and substrates for this reaction. Our results indicate that the most favorable pathway is a stepwise conjugate addition-Aldol sequence via the dual hydrogen-bond binding mode. The predicted level of enantioselectivity is in good agreement with experimental values. Trends in variation of substrates and catalysts have also been reproduced by our calculations. Decomposition analysis revealed the significance of aromatic interactions in stabilizing the key enantioselectivity-determining transition state structures.