Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

Self-driving labs (SDLs) employing automation and machine learning (ML) offer great promise for accelerating materials discovery and optimisation. However, in thin film science, SDLs are mainly restricted to solution-based methods which are easier to automate, restricting access to the broader chemical space of inorganic materials. This work advances an SDL based on magnetron co-sputtering, addressing a key challenge: rapidly generating accurate composition maps of multi-element, compositionally graded thin films. Traditional ex-situ methods are slow and error-prone; instead, we present a fast, calibration-free, in-situ ML approach to predict the deposition rate using quartz-crystal microbalance (QCM) sensors. For each sputtering source, deposition rates are sequentially learned as a function of pressure and power via active learning with Gaussian processes (GPs). The final GPs are combined with a geometric flux model to interpolate deposition rates across the sample. Among several acquisition functions with random query as the baseline, the Bayesian active learning MacKay (BALM) approach yielded the best performance, requiring as few as 10 experiments per source. The model predictions for co-sputtering composition distributions were validated against external composition measurements. This framework significantly increases throughput in combinatorial sputtering studies and highlights the potential of ML-guided SDLs to surpass traditional Edisonian methods. • Self-driving PVD lab synthesising of combinatorial multi-element thin-films thin-films. • In-situ process modelling to obtain accurate composition maps. • High-throughput machine learning guided experiment design via active learning. • Deposition rate can be learnt in as few as 10 experiments using fully Bayesian models.