Zeolites are crystalline materials made of silicon, aluminum and oxygen with small pores into which molecules can diffuse and react. Catalytically active sites in these materials form when Si4+ is replaced with Al3+ to create a charge imbalance that is usually compensated by H+ to form a Brønsted acid site. The crystallographic positions of the Al substitution within these materials can influence their catalytic behavior. MFI and CHA are two industrially important zeolite frameworks that are often synthesized with organic structure-directing agents (OSDAs), which are organic molecules—often composed of C and N—that template the pores of the zeolite. The N atoms of these OSDAs are often quaternary cations that balance the net anionic charge that Al substitution produces during synthesis. Density functional theory (DFT) simulations of OSDAs within MFI and CHA with attendant framework Al show that coulombic interactions have a significant influence on the relative stability of different Al configurations. Both N+–Al− attraction and Al−– Al− repulsion influence where Al are favored, in addition to steric preferences for Al substitution in different crystallographic positions of the zeolite. The final positions of Al in these zeolites can affect turnover rates for probe reactions, such as methanol dehydration. When the Al content of frameworks is large, rigorous DFT investigations of Al distributions can become intractable. Neural network potentials (NNPs) trained on DFT data can be used to rapidly screen the energies of different Al positions with OSDAs. One such NNP shows good predictive abilities in a CHA zeolite with high Al densities for estimating energies of such arrangements.

Alex was raised in northern Virginia and earned his BS in Chemistry from the College of William and Mary. After spending one year at the Environmental Protection Agency doing analytical chemistry lab work, he moved to Gainesville, FL to attend University of Florida. He earned his Ph.D. in Chemical Engineering at UF in 2022 under the guidance of David Hibbitts. His doctoral work focused on DFT simulations of heterogeneous catalysts like zeolites and oxide-supported metals. He moved to Massachusetts Institute of Technology to work with Rafael Gómez- Bombarelli as a postdoctoral associate. At MIT, he continues to use computational tools to study zeolites, with a focus on using machine learning methods to understand how to control zeolite synthesis.