Automated Discovery of Reactive Events via Hypergraph Mining of Ab Initio Atomistic Simulations.

The field of generative chemistry and automated exploration of chemical reaction space has gained much interest in recent years as it provides a feasible alternative to performing resource-intensive experiments by enabling important computational insights into new molecular systems. The results are often summarized in reaction networks, which reveal intricate relations between different key reactive events. Although various approaches to explore the available chemical space have been introduced, the information contained in the resulting reaction networks has not been fully exploited so far. We propose an automated workflow for the analysis of chemical reaction networks by applying frequent pattern mining on the corresponding directed hypergraphs to identify frequently occurring reactive patterns across a set of simulations. Furthermore, we identify reactive events that are statistically correlated with given environmental conditions by applying Fisher's exact test and controlling the family-wise error rate to ensure high statistical relevance. Minimum energy paths for frequent and statistically significant patterns are obtained with the molecular double-ended growing string method at ωB97X-3c level of theory. We showcase the pattern-mining-based analysis on the thermally controlled interstellar synthesis of carbamic acid, where we retrieve results in line with experimental data and further investigate the role of water as a protic solvent therein.