An automated method for graph-based chemical space exploration and transition state finding.

Graphical Abstract Description: A strategy for automatically exploring chemical space is presented. The method efficiently combines graph theory and quantum-chemical techniques to reduce the required human expertise and computational time for finding minima and saddle points in a potential energy surface. This is done by applying graph elementary transformations over automatically detected functional groups in molecules. Transition states in the resulted reaction network are then automatically found at a low level of theory to be later more accurately described at a higher level. Abstract: Algorithms that automatically explore the chemical space have been limited to chemical systems with a low number of atoms due to expensive involved quantum calculations and the large amount of possible reaction pathways. The method described here presents a novel solution to the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. Firstly, a first version of the reaction network is determined through molecular graph transformations acting upon functional groups of the reacting. Only transformations that break two chemical bonds and form two new ones are considered, leading to a significant performance enhancement compared to previously presented algorithm. Secondly, energy barriers for this reaction network are estimated through quantum chemical calculations by a growing string method (GMS), which can also identified non-octet species missed during the previous step and further define the reaction network. The proposed algorithm has been successfully applied to five different chemical reactions, in all cases identifying the most important reaction pathways.