“Over the past decade, machine-learned potentials have significantly advanced the way in which we simulate molecular dynamics, offering speed and scalability. Our new method bridges this gap by integrating multireference quantum chemistry methods with machine learned potentials, delivering both accuracy and efficiency.” Gagliardi said. To address this challenge, the team turned to machine-learned interatomic potentials (ML-potentials), which can capture molecular dynamics with remarkable efficiency. WASP delivers dramatic speedups: simulations with multireference accuracy that once took months can now be completed in just minutes. Impact: Bridging Accuracy and Efficiency in Catalyst Design By uniting accuracy and speed, WASP opens the door to designing catalysts that can withstand realistic conditions—high temperatures and high pressures.