(115c) Predictive Modeling of Nanoporous Materials: Large-Scale Simulations, High-Throughput Screening and Machine Learning

Nanoporous materials, such as zeolites and metal-organic frameworks, play numerous important roles in modern oil and gas refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a nanoporous material as energy storage or chemical separation medium depends on its framework structure and the arrangement/type of interaction sites. This talk will highlight (a) large-scale molecular simulations to probe adsorption and diffusion in hierarchical nanoporous materials and (b) high-throughput Monte Carlo simulations in conjunction with machine learning to explore high-dimensional materials/chemical/state point spaces.