Trends in computational molecular catalyst design

Abstract

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions on catalyst performance and decreasing the time needed for catalyst screening....

Computational Study for CO2-to-CO Conversion over Proton Reduction Using [Re[bpyMe(Im-R)](CO)3Cl]+ (R = Me, Me2, and Me4) Electrocatalysts and Comparison with Manganese Analogues

“…The detailed calculated TOFs and “degree of TOF control” (X TOF ) can be found in the Supporting Information (Tables S6–S9). We note that microkinetic modeling is another approach that is often used in homogeneous catalysis. − Still, the ESM allows us to compare the experimental selectivity for CO formation over H + reduction by relating the computed Gibbs free energies and turnover frequency (TOF). − …”

Machine learning for design principles for single atom catalysts towards electrochemical reactions

“…ML is a strong tool 159 to provide a fundamental understanding of structural sensitivity 160,161 through establishing deep relationships between catalytic activity and structural as well as atomic properties based on mechanisms and similarities in SACs. 13,32,162 ML is considered as a new direction for the rational design of SACs by exploring feature importance analysis for electroreduction reactions to introduce more perceptions on the origin of the activity and stability of SACs. [163][164][165] For example, ML integrated DFT was applied to establish a relationship between various descriptors and hydrogen adsorption free energy (DG H* ) for the HER by altering the size and dimensionality of a nitrogen-doped 2D-carbon substrate for 3d, 4d, and 5d transition metals (TMs) as SACs.…”

“…2a, SVM as a binary classification and regression algorithm classifies data points into two distinct categories by using hyperplanes. 32 The SVM assigns each point of training data to one of two classes and minimizes the error between the classes by dividing the categories using a hyperplane, which maximize the margin around the hyperplane. The hyperplane is completely defined by the data points that are closest to the plane and between the support vectors.…”

Genetic Optimization of Homogeneous Catalysts

“…In the spirit of inverse design, [4][5][6][7][8] NaviCatGA uses a Genetic Algorithm (GA) [9][10][11][12] to find optimal catalysts (Figure 1). This pipeline represents a complementary approach to highthroughput screening [13][14][15][16][17] that becomes comparatively more efficient as the dimensionality of the combinatorial space of catalyst components grows. Furthermore, evolutionary experiments with GAs lead to alternative chemical insight into catalyst performance, as demonstrated hereafter.…”