Researchers led by Professor Laura Gagliardi at the University of Chicago’s Pritzker School of Molecular Engineering, collaborated with the Parrinello Group at the Italian Institute of Technology in Genoa, have developed a new method that combines machine learning with quantum chemistry to simulate the behavior of transition metal catalysts more accurately and efficiently. This advancement, known as the Weighted Active Space Protocol (WASP), addresses a longstanding challenge in modeling catalytic systems.
Seal, A., Perego, S., Hennefarth, M. R., Raucci, U., Bonati, L., Ferguson, A. L., Parrinello, M., & Gagliardi, L. (2025). Weighted active space protocol for multireference machine-learned potentials. Proceedings of the National Academy of Sciences, 122(38). https://doi.org/10.1073/pnas.2513693122
Catalysts are essential in various industrial processes, including the production of pharmaceuticals and plastics. Transition metals are particularly effective due to their partially filled d-orbitals, which facilitate electron exchange. However, these same properties make them difficult to model accurately, requiring detailed descriptions of their electronic structures.
Traditional quantum chemistry methods, such as multiconfiguration pair-density functional theory (MC-PDFT), offer high accuracy but are computationally intensive, making them impractical for simulating the dynamics of catalytic systems under realistic conditions. Machine-learned interatomic potentials (ML-potentials) have been used to speed up simulations but often lack the precision needed for transition metal catalysts.
Ph.D. student Aniruddha Seal from University of Chicago stated,
“Think of it like mixing paints on a palette, if I want to create a shade of green that’s closer to blue, I’ll use more blue paint and just a little yellow. If I want a shade leaning toward yellow, the balance flips. The closer my target color is to one of the base paints, the more heavily it influences the mix. WASP works the same way: it blends information from nearby molecular structures, giving more weight to those that are most similar, to create an accurate prediction for the new geometry.”
To overcome these limitations, the research team developed WASP, which integrates the accuracy of MC-PDFT with the efficiency of ML-potentials. The key innovation is a novel algorithm that generates consistent wave functions for new molecular geometries by combining information from previously sampled structures. This approach ensures that every point along a reaction pathway is assigned a unique, consistent wave function, enabling accurate training of ML-potentials on multireference data.
WASP has been demonstrated on the methane activation reaction catalyzed by titanium carbide, a process that poses challenges for traditional methods due to its significant multireference character. The results show that simulations that once took months can now be completed in just minutes, without sacrificing accuracy.
This method opens new possibilities for designing catalysts that can operate under realistic conditions, such as high temperatures and pressures. By enabling more accurate simulations, WASP can aid in the development of more efficient and sustainable catalytic processes.
Adrian graduated with a Masters Degree (1st Class Honours) in Chemical Engineering from Chester University along with Harris. His master’s research aimed to develop a standardadised clean water oxygenation transfer procedure to test bubble diffusers that are currently used in the wastewater industry commercial market. He has also undergone placments in both US and China primarely focused within the R&D department and is an associate member of the Institute of Chemical Engineers (IChemE).
