Recent advances in machine learning–assisted catalyst screening have accelerated the discovery of active sites across diverse chemistries, yet density functional theory (DFT) calculations remain a central computational bottleneck. In particular, locating transition states (TS) is critical for determining activation barriers and reaction kinetics, but remains one of the most challenging and time-consuming DFT tasks due to the presence of multiple local maxima on potential energy surfaces. Efficiently automating this step is essential to scale computational catalysis beyond small datasets and toward true high-throughput discovery. To address this limitation, we expanded our group’s open-source code, MolSimplify, with a fully automated TS search workflow applicable to mechanistic steps characterized by a single bond length as the reaction coordinate. The tool autonomously generates initial structures for specified catalysts and substrates, performs geometry optimizations using TeraChem, executes bond-length scans and TS optimizations with ORCA, and incorporates systematic convergence checks and automated error recovery. This framework eliminates the need for manual setup or intervention during TS searches. The workflow was validated through application to methane-to-methanol conversion catalyzed by transition-metal complexes, reproducing known transition states and determining activation energies for newly identified candidates. The screening aimed to identify synthetic complexes capable of mimicking the high energy efficiency of metalloenzymes that selectively oxidize methane under mild conditions. The automated approach achieved consistent TS convergence of 80% with minimal user input and reduced computational overhead compared to conventional manual searches. Overall, this work provides a robust and extensible platform for automated transition-state identification of one degree of freedom reactions, enabling more efficient mechanistic analysis and accelerating the integration of DFT calculations into data-driven catalyst discovery pipelines.
