Ball Akash K, Terrones Gianmarco G, Yue Shuwen, Kulik Heather J
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
ACS Appl Mater Interfaces. 2025 Jun 18;17(24):35971-35985. doi: 10.1021/acsami.5c09320. Epub 2025 Jun 5.
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.
金属有机框架（MOF）是有望应用于受益于精确化学图案化的领域（如海水淡化）的候选材料，但许多MOF在水中稳定性较差。除了水稳定性外，对于MOF与水相关的实际应用而言，预计在环境条件下具有高吸水能力也是必要的，这促使人们只能通过计算来进行大规模搜索。在此，我们采用机器学习和高通量筛选相结合的方法来识别具有高吸水能力的水稳定MOF。从先前筛选出的在水中具有实验已知的极高稳定性的MOF子集中，我们探索了用12种不同的亲水性官能团进行连接体功能化的效果，预期这些官能团能进一步调节吸水性能。对于这736种MOF，我们使用巨正则蒙特卡罗（GCMC）模拟来计算它们的吸水能力。我们观察到MOF孔隙特征（例如，最大空腔直径和孔隙体积）与吸水能力之间存在很强的正相关关系，尽管我们注意到在具有极疏水连接体的MOF中，尽管孔隙很大，但水分子会被排斥，这种相关性会失效。最后，我们开发机器学习模型，以同时筛选新的MOF的水稳定性和吸水能力。从一系列假设的和实验性的MOF中，我们在机器学习模型的适用范围内识别出74种有前景的材料，预计它们既具有水稳定性又具有高吸水能力。
