Active Learning for Tractable and Reproducible Pulsed Laser Deposition

Abstract

This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth conditions and material properties, consistently producing high-quality films of complex materials like LaVO$_3$ remains difficult due to the highly non-equilibrium nature of PLD and the defects and competing phases that accumulate during growth. Here, we use an active learning framework based on Gaussian process Bayesian optimization that incorporates measured bulk and surface lattice properties along with impurity phase information to efficiently map the multidimensional growth space of LaVO$_3$ by PLD. By tuning the relative weighting of these properties, the model identifies an optimized region where phase-pure films of LaVO$_3$ exhibit two-dimensional surfaces, near-ideal lattice parameters, and minimal sub-band gap optical absorption. The trained model reveals clear competition among different defect formation mechanisms that are connected to unseen parameters like supersaturation and surface mobility, thus giving insight into the highly non-equilibrium process of PLD growth. Together, this demonstrates that property-guided machine learning can accelerate materials optimization while providing a new way to address fundamental growth mechanisms in PLD that enable understanding and utilization of quantum phenomena found in complex oxides.