PICS Colloquium: Enabling computationally efficient first-principles kinetic simulations in nanoporous catalysts using machine learning and data science with Brandon Bukowski

Abstract: Machine learning tools have tremendous potential to accelerate computationally complex physics-based simulations. One example is the need to accelerate materials discovery through first-principles Density Functional Theory (DFT) calculations.

This seminar will encompass how machine learning interatomic potentials accelerate the discovery of crystalline nanoporous solids such as zeolites and metal-organic frameworks (MOFs) that are employed in a wide range of catalytic processes due in part to their tunable micro-environments. Kinetics at intracrystalline sites can be modified by changing pore size, pore architecture, or polarity. These environments impart shape-selectivity that preferentially stabilizes transition states, but the large design space including pore architecture, polarity, and catalytic active site identity preclude comprehensive kinetic studies. DFT describes the electronic states of reactive intermediates and transition states but cannot access the longer length scales necessary to quantify the fluxionality of coadsorbed species or solvents. Classical simulations can accurately simulate these conformational changes but require parameterized values. Machine learning interatomic potentials have emerged as a technique to derive parameterized models from DFT data, and our aim is to adapt these models to predict the entropy and diffusion of reactive intermediates in nanoporous catalysts.

Bio: Brandon Bukowski is an Assistant Professor in the department of Chemical and Biomolecular Engineering and Associate Researcher in the Ralph O’Connor Sustainable Energy Institute at Johns Hopkins University. He holds BS and PhD degrees in Chemical Engineering from Worcester Polytechnic Institute and Purdue University, respectively. At Purdue he was advised by Jeffrey Greeley, received the Faculty Lectureship Award, and was supported in part by a Dick Reitz fellowship from the Center for the Innovative and Strategic Transformation of Alkane Resources NSF ERC. At Purdue, he collaborated with Rajamani Gounder’s group and Fabio Ribeiro’s group to model the kinetics of zeolite and supported nanoparticle catalysts using Density Functional Theory and Molecular Dynamics. He performed post-doctoral research at Northwestern University under the supervision of Randall Snurr studying diffusion in nanoporous materials including zeolites, metal-organic frameworks (MOFs), and porous polymers.

Bukowski started at Johns Hopkins University in July of 2021. He has received a Ralph E. Powe award from Oak Ridge Associated Universities, a Doctoral New Investigator grant from the ACS Petroleum Research Fund, an Amazon Research Award, and a Hopkins Catalyst Award. He is a member of the Catalysis division board of the American Chemical Society and program chair of the Northeast Corridor Zeolite Association.