Combinatorial and high-throughput
experimentation (HTE) is achieving
more relevance in material design, representing a turning point in
the process of accelerated discovery, development, and optimization
of materials based on data-driven approaches. The versatility of drop-on-demand
inkjet printing (IJP) allows performing combinatorial studies through
fabrication of compositionally graded materials with high spatial
precision, here by mixing superconducting REBCO precursor solutions
with different rare earth (RE) elements. The homogeneity of combinatorial
Y1–xGdxBa2Cu3O7 samples was designed with
computational methods and confirmed by energy-dispersive X-ray spectroscopy
(EDX) and high-resolution X-ray diffraction (XRD). We reveal the advantages
of this strategy in the optimization of the epitaxial growth of high-temperature
REBCO superconducting films using the novel transient liquid-assisted
growth method (TLAG). Advanced characterization methods, such as in
situ synchrotron growth experiments, are tailored to suit the combinatorial
approach and demonstrated to be essential for HTE schemes. The experimental
strategy presented is key for the attainment of large datasets for
the implementation of machine learning backed material design frameworks.