Automated input structure generation for single‐ended reaction path optimizations

Autor: Julian Geiger, Volker Settels, Peter Deglmann, Ansgar Schäfer, Maike Bergeler
Rok vydání: 2022
Předmět:
Zdroj: Journal of Computational Chemistry. 43:1662-1674
ISSN: 1096-987X
0192-8651
Popis: 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.
Databáze: OpenAIRE
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