Bayesian Optimization Approach for Discovery of High-Capacity Small-Molecule Adsorption in Metal-Organic Frameworks
ORAL
Abstract
Metal-organic frameworks, due to their highly porous structures, have emerged as a promising class of small-molecule adsorbent materials for a variety of separations, storage, and usage applications. While it is possible to construct viable hypothetical MOFs (hMOFs) from known metal nodes and organic linkers, it is computationally expensive to calculate at a high accuracy the small molecule uptake capacity of MOF structures. Using ~51,000 hypothetical MOF structures and data calculated from [1] for CH4, we show it is possible to identify candidates for high-performance CH4 adsorbents by calculating uptake capacities for <1% of the database using Bayesian optimization. Furthermore, we show that building chemical intuition into the surrogate model and including structural characteristics dramatically improves the performance and interpretability of the optimization process. The applicability of our Bayesian approach and workflow to molecules beyond CH4 and other hypothetical adsorbents is discussed.
[1] Wilmer, C., et al., Nat. Chem 4, 83–89 (2012).
[1] Wilmer, C., et al., Nat. Chem 4, 83–89 (2012).
*This work funded by the National Energy Technology Laboratory (NETL) under the Discovery of Carbon Capture Substances and Systems (DOCCSS) Initiative. Computational resources provided by NETL and Lawrence Berkeley National Laboratory.
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Presenters
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Eric Taw
- Chemical Engineering, University of California, Berkeley, and Materials Sciences Division, Lawrence Berkeley National Laboratory