Nanoindentation Criteria for Combinatorial Thin Film Libraries

Employing high‐throughput characterization techniques on combinatorially synthesized thin film material libraries offers a pathway for accelerating the discovery and development of novel materials. In particular, nanoindentation is useful for rapidly screening composition‐property relationships. However, despite its widespread use, current literature reveals methodological inconsistencies regarding the number of indents performed per composition and the total number of compositions sampled. This work utilizes a Cu _x Ni _{(1− x )} library as a model system to investigate nanoindentation data collection and optimize experimental throughput. Interpolation methods are applied to data subsets to identify critical regions of interest and capture material trends. The employed framework indicates that characterizing as few as 15% of the total compositions is sufficient for predicting both hardness and modulus trends across the Cu _x Ni _{(1− x )} library. This approach can be used as a baseline for investigating more compositionally complex systems, enabling faster generation of datasets, materials discovery, and training sets for machine learning models.