Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO2working capacity and CO2/N2selectivity properties.
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Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers
Several MOFs are evaluated as adsorbents of anthropogenic N2O emissions, the third most abundant greenhouse gas, through complimentary experimental and DFT analysis. N2O activation in M2(dobdc) MOFs is also studied.
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- PAR ID:
- 10504475
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Journal of Materials Chemistry C
- Volume:
- 12
- Issue:
- 9
- ISSN:
- 2050-7526
- Page Range / eLocation ID:
- 3164 to 3174
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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