(220a) Computationally-Efficient High-Throughput Screening of Nanoporous Materials for Hydrogen Storage
AIChE Annual Meeting
Monday, October 29, 2018 - 3:30pm to 3:45pm
We have used high-throughput screening techniques paired with grand canonical Monte Carlo simulations (GCMC) to calculate the hydrogen storage capacity for over 130,000 MOFs. We will discuss commonalities in the structural and textual properties among the top-performing candidates from this screening and explore how the information gleaned from high-throughput screening can inform the design of new materials that are tailored for efficient hydrogen storage applications.
High-throughput screening generates a large amount of data, which makes it amenable to using machine learning and data mining to analyze the data for thousands of structures. We introduce an efficient regression model that can estimate the storage capacity for thousands of structures in a small fraction of the time required for full GCMC simulations. This allows us to quickly identify the most promising candidates for more detailed study, and this technique can be applied to other gases, such as methane, as well as a wide range of temperatures and pressures.
We demonstrate the utility of this screening method by searching a database of 55,000 known MOF structures  to identify the top candidates for hydrogen storage. After finding the best structures, we worked with experimental collaborators to synthesize one of the top structures and test its hydrogen storage capacity. This paradigm of using molecular simulation and machine learning techniques coupled with experiments can greatly accelerate the discovery and synthesis of advanced materials.
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