Miguel Gallegos

Energy profiles alone miss key aspects of how reactions actually proceed: which bonds break first, how charge flows between atoms, and where the energetic cost of a barrier really comes from. Real-space bonding tools such as QTAIM and IQA can answer these questions, but have so far been applied only to small, hand-picked systems. We present MARS-ROVER (Reaction Optimization and Virtual Exploration of Reactivity), a module within the MARS platform that automates reaction mechanism discovery using machine-learned force fields (MLFFs). Starting from just reactant and product structures, it generates reaction paths through nudged elastic band calculations, refines saddle points, and validates intrinsic reaction coordinates (IRCs). New intermediates found along the way are fed back automatically, building up complete reaction networks without manual intervention. The toolkit works with modern MLFFs such as SO3LR, MACE, and ANI, and runs on standard laptop hardware. We have applied it to competing E2/SN2 pathways, pericyclic reactions (Diels–Alder, Cope, 1,3-dipolar cycloadditions), organocatalytic cycles, and regio- and stereoselective processes. We then go beyond energy profiles by analyzing each pathway with real-space descriptors: IQA decomposition to see how electrostatic, exchange, and intra-atomic energies evolve as bonds form and break; delocalization indices and bond orders to track bond reorganization; and atomic charges to follow charge transfer along the reaction coordinate. This combination reveals features invisible to energy-only analysis, such as the degree of synchronicity in multi-bond processes, which atom–atom interactions control barrier heights, and how charge-transfer patterns differ between competing pathways. By pairing fast MLFF exploration with the chemical interpretability of real-space bonding analysis, we can now characterize entire reaction networks at a level of detail that was previously limited to individual case studies.