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Abstract
We present an autonomous physical vapor deposition system that integrates hardware automation, in-situ optical spectroscopy, and Bayesian machine learning into a complete self-driving laboratory framework making decisions on the fly. Using silver thin films as a model material, our platform efficiently navigates a complex parameter space through active learning. By introducing a thin physical layer denoted as calibration layer, the machine learning models adapt to sample-specific conditions on the fly and reliably predict the deposition conditions to achieve user-specified optical properties. Moreover, from the high-throughput experimental data, the algorithm systematically captures the complex parameter-property relationships that are challenging to deduce by conventional trial-and-error methods. This study demonstrates the potential of self-driving laboratories for both reducing human labor and gaining new understanding of materials, providing a streamlined approach to enable self-driving physical vapor deposition systems.