Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST‐1 SURMOF Optimization

Metal–organic Frameworks (MOFs), especially as thin films, are increasingly
recognized for their potential in device integration, notably in sensors and
photo detectors. A critical factor in the performance of many MOF-based
devices is the quality of the MOF interfaces. Achieving MOF thin films with
smooth surfaces and low defect densities is essential. Given the extensive
parameter space governing MOF thin film deposition, the use of machine
learning (ML) methods to optimize deposition conditions is highly beneficial.
Combined with robotic fabrication, ML can more effectively explore this space
than traditional methods, simultaneously varying multiple parameters to
improve optimization efficiency. Importantly, ML can provide deeper insights
into the synthesis of MOF thin films, an essential area of research. This study
focuses on refining an HKUST-1 SURMOF (surface-mounted MOF) to achieve
minimal surface roughness and high crystallinity, including a quantitative
analysis of the importance of the various synthesis parameters. Using the
SyCoFinder ML technique, thin film surface quality is markedly enhanced in
just three generations created by a genetic algorithm, covering 30 distinct
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parameter sets. This method greatly reduces the need for extensive
experimentation. Moreover, the results enhance the understanding of the vast
synthesis parameter space in HKUST-1 SURMOF growth and broaden the
applications of MOF thin films in electronic and optoelectronic devices.