Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Comlek, Yigitcan; Pham, Thang Duc; Snurr, Randall Q.; Chen, Wei

doi:10.1038/s41524-023-01125-1

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Journal Article · Thu Sep 21 00:00:00 EDT 2023 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-023-01125-1· OSTI ID:2001318

; Pham, Thang Duc; ;

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 CO ₂ working capacity and CO ₂ /N ₂ selectivity properties.

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Research Organization:: Northwestern Univ., Evanston, IL (United States); Univ. of California, Oakland, CA (United States); Univ. of Minnesota, Minneapolis, MN (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: AC02-05CH11231; AR0001209; SC0008688

OSTI ID:: 2001318

Alternate ID(s):: OSTI ID: 2422338

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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