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: Applications of 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. Nonetheless, it is possible to explore large regions of the chemical space with low-cost graph theory techniques, to later apply quantum calculations on relevant reaction paths. The method described here tackles the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. This is done by defining molecular graph transformations that represent elementary reactions in a graph theory approach. Such transformations act upon functional groups in molecules that fulfil the Lewis Structure, which are represented in terms of bond order matrices. This way, a study showing computational time and method’s performance in five different chemical systems is presented with a concise chemical representation of graphs, to finally apply an efficient combination of quantum chemical calculations on the reaction paths.