We present an active learning algorithm for identifying viable process conditions in magnetron sputtering experiments. The algorithm trains a classifier to predict which gas pressure and magnetron power combinations yield stable discharge with deposition rates exceeding a minimum threshold. A computation-based oracle that labels experiments using QCM readings facilitates a fully automated learning procedure, laying the groundwork for a self-driving lab where novel materials will be explored for next-generation solar cells. Upon evaluation, the active learning algorithm results in significantly higher sample efficiency than traditional supervised learning across a range of magnetron sputtering experiments. Moreover, a sampling sequence analysis shows that active learning enables an informed search of the process parameter space, generating patterns that approximate Paschen’s law. The work presented in this thesis serves as a first step toward a fully automated materials synthesis process, where the input region of viable synthesis parameters can be identified with minimal experimentation. The solution allows researchers to efficiently narrow the search space of optimal process conditions for targeted materials design.