Uncovering the Mechanisms of Strongly-Correlated Organic Isomerizations with Surrogate-Hessian-Accelerated Diffusion Monte Carlo

A central challenge in chemical physics is the accurate ab initio modeling of reaction pathways for correlated-electron systems. Low-cost methods, like Density Functional Theory (DFT), struggle to capture correlation effects, while high-accuracy methods like Quantum Monte Carlo (QMC) have traditionally been too computationally expensive for full Minimum-Energy Pathway (MEP) evaluation. However, recent innovations employing less expensive surrogate theories like DFT as baselines for high-level QMC have substantially lowered the computational burden of such calculations, opening the door to practical strongly-correlated reaction pathway optimization. In this work, we present the first known optimization of this kind, modeling a complete correlated MEP for the cyclobutene-butadiene isomerization via DFT surrogate-Hessian-accelerated Diffusion Monte Carlo. By revealing this strongly-correlated, concerted mechanism from first principles, we aim to pave the way for a new era of correlated mechanistic insights, with applications ranging from heterogeneous catalysis to benchmarking chemical simulation on quantum computers.