Benchmarking and Reconciling Spin-State Energetics: Toward Unified Multilevel Quantum Catalysis Models

Radon et al. (2024) point out the persistent discrepancies between different quantum chemical methods for spin-state energetics, which are critical in transition metal catalysis. Fanta & Bajdich (2025) further highlight the promise of high-level approaches like FNDMC. This research would systematically benchmark such methods across a wide range of catalytic systems, using experimental data as a touchstone, and then train ML models to "learn" the systematic errors of lower-level methods. The goal is a hybrid approach—using fast, lower-level methods corrected by ML models trained on sparse, high-accuracy data—to deliver reliable predictions for catalysis design. By explicitly addressing and reconciling conflicts in the literature, this could become a new standard for computational catalyst screening.

References:

1. Performance of quantum chemistry methods for a benchmark set of spin-state energetics derived from experimental data of 17 transition metal complexes (SSE17). M. Radoń, G. Drabik, M. Hodorowicz, J. Szklarzewicz (2024). Chemical Science.
2. Resolution of Selectivity Steps of CO Reduction Reaction on Copper by Quantum Monte Carlo.. Roman Fanta, Michal Bajdich (2025). Journal of Physical Chemistry Letters.