Goeltl Lab
Goeltl Lab
In the Computational Materials Design group, we focus on the design of materials at an atomistic level. Our goal is not only to identify ideal materials, but also use computational modeling to guide experimental efforts and help to identify ideal materials preparation and operating protocols. We focus on developing new methods for the photothermal upgrading for CO2, machine learning and AI based methods for the design of heterogeneous catalysts, the computational modeling of zeolite synthesis, the design of transition metal exchanged zeolites for the conversion of methane and in the selective catalytic oxidation of nitrous oxides, and the understanding of various spectroscopies (UV-vis, photoluminescence, IR, Raman, NMR, and others) for zeolite catalysis. We use state of the art electronic structure modeling in combination with ensemble averaging techniques and machine learning to understand and design materials.