Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches

Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer-by-layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi-parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML-based approach allowing to quickly identify optimized growth conditions for HKUST-I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films

Utilizing Machine Learning to Optimize Metal-Organic Framework-Derived Polymer Membranes for Gas Separation

Metal-organic frameworks (MOFs) have gained substantial attention as promising materials for gas separation membranes due to their exceptional porosity, tailorability, and functionalizability. In this study, we present a novel approach to further enhance the properties of porous polymer membranes emerging from MOFs through crosslinking of the organic linker molecules and subsequent metal-atom removal. To ensure reproducibility of the multi-step synthesis process and high quality of the resulting polymeric membranes, we automated the process and followed a machine learning optimization approach. The high-quality MOF-thin films (SURMOFs) were prepared in a layer-by-layer fashion directly on gold-coated porous alumina substrates. This direct synthesis proved crucial to preserve the structural integrity of the membranes and thus avoiding defect formation caused by a substrate-transfer process, which is usually required when advanced materials are used to fabricate a membrane. The initial SURMOF membrane exhibits moderate gas separation performance, once crosslinked, its gas selectivity could be significantly enhanced although with the compromise of lower gas permeance. Interestingly, once we removed the metal centers and thereby converted the SURMOF into a purely organic polymeric membrane, the membrane gas permeance could be restored almost to its initial condition while preserving the enhanced selectivities. In particular, the resulting polymeric membrane outperforms most commercially available polymer membranes for H2 /CO2 gas separation. This research outlines a promising approach to employ MOFs as template in the generation of advanced polymer membranes for various gas and liquid phase separation applications