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.