Accelerated HKUST-1 Thin-Film Property Optimization Using Active Learning

A flow-coating method termed solution shearing has been shown to grow large-area thin films with no void spaces. Attaining full coverage is one of the key prerequisites for the adoption of any metal-organic framework (MOF) thin film for a variety of practical applications, including separation, membranes and sensors. However, the solution-shearing process has multiple discrete and continuous parameters that can be varied, including the metal ion and linker concentrations, solvents, substrate temperature, coating speed, and the number of coating passes. Optimization of these parameters for full coverage is a time-consuming and daunting process due to vast parameter space. Here, we incorporate an active learning approach into the solution-sheared HKUST-1 thin-film-processing parameters to control the coverage and extend the approach to gain control over the thickness. The understanding of high-quality MOF thin-film formation using solution shearing is improved by correlating the processing parameter sets and their corresponding film coverage. A large area and fully covered HKUST-1 thin film with a minimized thickness of 2.2 μm is fabricated by using guidance from active learning. To confirm full coverage, a redox-active molecule, called 7,7,8,8-tetracyanoquinodimethane (TCNQ), is incorporated along with the HKUST-1 thin film. The TCNQ@HKUST-1 thin film with a minimized thickness has the same order of magnitude of electrical conductivity as that of the TCNQ@HKUST-1 thin film created previously while reducing the film thickness by 60%. We show that active learning has the potential to rapidly navigate the vast processing space in multicomponent systems, especially when experiments are expensive and traditional computational models are not readily available for process optimization.