Computation assisted discovery of nanoporous materials for gas storage and separations
Nanoporous materials, such as metal-organic frameworks (MOFs), have enormous internal surface areas. Their consequent adsorption properties demonstrate promise towards solving energy-related problems in gas storage and gas separations. Owing to their modular and versatile chemistry, millions of possible nanoporous materials can be synthesized. This vast chemical space allows a material to be tailor-made or fine-tuned to target specific adsorbate molecules and conditions. In this thesis, we utilize molecular models and simulations of gas adsorption in both existing and predicted nanoporous material structures to accelerate the discovery of new materials targeted for gas storage and separations at specific conditions.
In the first part of this work, we approach the problem of identifying an optimal porous material to densify natural gas for storage onboard vehicles as fuel. We developed a series of statistical mechanical models to find the thermodynamic parameters that optimize the deliverable capacity of a material. We conclude that the heat of adsorption, which is a commonly used metric to evaluate materials for natural gas storage, is a misleading metric because the optimal heat of adsorption depends on the pore size. Our models also reveal that adsorbate-adsorbate attractions-- in the case where multiple methane molecules can fit into a pore-- can enhance the deliverable capacity.
Next, we carried out a high-throughput computational screening of metal-organic frameworks, porous polymer networks, zeolites, and zeolitic imidazolate frameworks for natural gas storage. The data that we collected provide candidate structures for synthesis, reveal relationships between structural characteristics and performance, and suggest that it may be difficult to reach the current Advanced Research Project Agency-Energy (ARPA-E) deliverable capacity target. To assess thermodynamic limits to the methane deliverable capacity, we then built a model of an extreme scenario where an energy field can be created without taking up space with material. This model suggests that, while the failure to reach the ARPA-E storage target is due to material design constraints rather than purely thermodynamic constraints, the ARPA-E storage target is ambitiously close to the thermodynamic limit.
In the second part of this work, we approach the problem of identifying a material that selectively adsorbs xenon over krypton. With over half a million nanoporous material structures to consider as candidate adsorbents, the computational cost of a brute-force computational screening strategy was prohibitive. Instead, we employed a machine learning algorithm, a random forest, to learn the relationship between quickly computed structural descriptors and Xe/Kr selectivity, which is more expensive to compute. The trained random forest allowed us to rule out a large percentage of the materials on the basis of quickly-computed structural descriptors. Our machine learning accelerated screening pinpoints top candidates on which to focus experimental efforts and elucidates structure-property relationships for design guidelines for a Xe-selective material.
As we are now working with mixed gas adsorption, we developed a user-friendly software package in Python, pyIAST, for ideal adsorbed solution theory (IAST) calculations. IAST is a thermodynamic framework to predict mixed gas adsorption from pure-component adsorption isotherms, which are easier to measure. We provide practical guidelines for applying IAST.
Finally, we carry out a high-throughput computational screening of metal-organic frameworks for capturing Xe from air at dilute conditions, a separation encountered in used nuclear fuel reprocessing. Our computational screening, facilitated by a parallelized code on GPUs, predicted a metal-organic framework, SBMOF-1, to be among the most Xe-selective. Our experimental collaborators synthesized and tested SBMOF-1 and found it to exhibit the highest Xe/Kr selectivity and Xe Henry coefficient reported in the literature. Column-breakthrough experiments reveal that SBMOF-1 is a near-term material for capturing xenon from the off-gases of used nuclear fuel reprocessing plants. This is a rare case of a computation-assisted materials discovery.