High-Throughput Reactivity Exploration for Extended Databases of Transition Metal Catalysts

01 September 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


A method is introduced for the automated reactivity exploration of extended in silico databases of transition metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical datasets. To address these challenges we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst datasets opens new opportunities for the design of high performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.


high throughput virtual screening
automated reaction network analysis
rational catalyst design
automated pathway finding
conformational search
Machine Learning
Artificial Intelligence

Supplementary materials

Supporting Information: Graphs
Extension of reaction class analysis
Supporting Information: Tables
Datasets for reaction classes


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