Integrated process-property modeling of YBaCuO superconducting film for data and model driven process design.

Process engineering of materials determines not only materials properties, but also cost, yield and production capacity. Although process design is generally based on the experience of process engineers, mathematical/data-science modeling is a key challenge for future process optimization. Here we create new opportunities for process optimization in YBaCuO film fabrication through data/model-driven process design. We show integrated modelling of substrate temperature and critical current density in YBaCuO films. Gaussian process regression augmented by transfer learning and physics knowledge was constructed from a small amount of data to show substrate temperature dependence of critical current density. Non-numerical factors such as chamber design and substrate material were included in the transfer learning, and physics-aided techniques extended the model to different magnetic fields. Magnetic field dependence of critical current density was successfully predicted for a given substrate temperature for a five-sample series deposited using different pulsed laser deposition systems. Our integrated process and property modelling strategy enables data/model-driven process design for YBaCuO film fabrication for coated conductor applications.