Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity
Name
Ball_ACSAMI_2025.pdf
Description
Accepted version
Size
4.75 MB
Format
Adobe PDF
Checksum (MD5)
ce65d3fc90bfc991c862e6052d607501
Author(s)
Ball, Akash K
Terrones, Gianmarco G
Yue, Shuwen
Kulik, Heather J
Date Issued
June 5, 2025
Journal
ACS Applied Materials & Interfaces
Publisher
American Chemical Society (ACS)
Citation
Ball, Akash K, Terrones, Gianmarco G, Yue, Shuwen and Kulik, Heather J. 2025. "Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity." ACS Applied Materials & Interfaces, 17 (24).
Version
Author's final manuscript
Abstract
Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identify water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with 12 diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to both be water-stable and have high water uptake.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
10.1021/acsami.5c09320
