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Dr. Josephson's NSF proposal is funded


Dr. Tyler Josephson, CBEE's newest faculty member, receives funding for his first NSF proposal. Congratulations to Dr. Josephson! We are looking forward to the exciting research. 

Award# 2138938, ERI - Simulation methods for competitive adsorption in Bronsted acidic zeolites

The production of synthetic zeolites for use as catalysts and adsorbents is a multi-billion-dollar industry. Zeolites are materials having molecule-scale pores that enable catalytic applications in petroleum refining, automotive emissions treatment, and biomass processing. Zeolites frequently catalyze chemical reactions in water, as is the case when producing renewable fuels and chemicals from biomass. The interactions of water molecules with the zeolite catalysts are incredibly complex, but it is important to understand how these systems behave so that zeolites can be optimized for application-based performance. Unfortunately, existing computational modeling tools struggle to both accurately and efficiently predict how complex chemical mixtures interact with zeolite catalytic active sites. This project will develop simulation techniques that will enable orders-of-magnitude improvements over current approaches, thereby generating fundamental knowledge of how water interacts with zeolite catalyst active sites. Symbolic regression, a machine learning tool for identifying equations that represent a given dataset, will also be explored for describing fundamental interactions between acids and bases. Community-based learning and outreach activities are planned to engage high school students in symbolic regression for math, science, and machine learning.

This project will advance Monte Carlo simulation methods to enable efficient sampling of water adsorption in acidic zeolites. Specific aims include 1) introducing Monte Carlo moves for directly sampling protonation states of water clusters in zeolites, 2) demonstrating simulations of competitive adsorption of water and alkanes using literature force fields, and 3) generating interatomic potentials for acid-base interactions by using symbolic regression to learn equations from quantum chemistry calculations. The anticipated outcomes will lay the groundwork for simulations of competitive chemisorption in porous materials with active sites, as well as discover new equations for simply characterizing potential energy surfaces of acid-base interactions.

Posted: January 31, 2022, 10:24 AM