Automated input structure generation for single‐ended reaction path optimizations. Issue 24 (22nd July 2022)

• Record Type: Journal Article
• Authors: Geiger, Julian; Settels, Volker; Deglmann, Peter; Schäfer, Ansgar; Bergeler, Maike
• Is Part Of: Journal of computational chemistry. Volume 43:Issue 24(2022)
• Page Start: 1662
• Page End: 1674
• Publication Date: 2022-07-22
• Subjects: quantum chemistry -- reaction networks -- reactivity prediction -- single‐ended reaction path optimization -- transition‐state optimization; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.26969
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract: 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. Abstract : Automated transition‐state (TS) optimizations are essential in chemical reactivity and reaction network studies. Single‐ended TS search methods are well‐suited for this task. However, they crucially depend on the quality of the input structure (termed "precomplex"). Therefore, robust, and efficient algorithms for their generation are required. We present a strategy for automated precomplex generation, which is applicable to uni‐ and bimolecular as well as pseudo‐termolecular reactions for a broad range of molecules.