(595b) Identifying New Descriptors for Gas Storage in Nanoporous Materials
AIChE Annual Meeting
2017
2017 Annual Meeting
Computational Molecular Science and Engineering Forum
Data Mining and Machine Learning in Molecular Sciences I
Wednesday, November 1, 2017 - 3:45pm to 3:57pm
The low volumetric density of hydrogen and methane is a major limitation to their use as low-emission transportation fuels. Filling a fuel tank with nanoporous materials, such as metal-organic frameworks (MOFs), could greatly improve the deliverable capacity of these tanks if appropriate materials could be found. However, the large number of possible structures makes it challenging to select the best MOF for a given storage application. We have developed new descriptors for gas adsorption in MOFs to characterize the strength of MOF-guest interactions to rapidly predict the storage capacity and efficiently filter large materials databases. By applying this method to a series of simple guest molecules, we will comment on the method's generalizability and applications to identify key structure/property relationships in materials design.