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The study of chemical reactions in aqueous media is very important for itsimplications in several fields of science, from biology to industrialprocesses. Modelling these reactions is however difficult when water directlyparticipates in the reaction. Since it requires a fully quantum mechanicaldescription of the system, $\textit{ab-initio}$ molecular dynamics is the idealcandidate to shed light on these processes. However, its scope is limited by ahigh computational cost. A popular alternative is to perform molecular dynamicssimulations powered by machine learning potentials, trained on an extensive setof quantum mechanical calculations. Doing so reliably for reactive processes isdifficult because it requires including very many intermediate and transitionstate configurations. In this study, we used an active learning procedureaccelerated by enhanced sampling to harvest such structures and to build aneural-network potential to study the urea decomposition process in water. Thisallowed us to obtain the free energy profiles of this important reaction in awide range of temperatures, to discover a number of novel metastable states andto improve the accuracy of the kinetic rates calculations. Furthermore, wefound that the formation of the zwitterionic intermediate has the sameprobability of occurring via an acidic or a basic pathway, which could be thecause of the insensitivity of reaction rates to the pH solution.

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water