  High-throughput experimentation enables efficient search space exploration
for the discovery and optimization of new materials. However, large search
spaces of, e.g., compositionally complex materials, require decreasing
characterization times significantly. Here, an autonomous measurement algorithm
was developed, which leverages active learning based on a Gaussian process
model capable of iteratively scanning a materials library based on the highest
uncertainty. The algorithm is applied to a four-point probe electrical
resistance measurement device, frequently used to obtain indications for
regions of interest in materials libraries. Ten materials libraries with
different complexities of composition and property trends are analyzed to
validate the model. By stopping the process before the entire library is
characterized and predicting the remaining measurement areas, the measurement
efficiency can be improved drastically. As robustness is essential for
autonomous measurements, intrinsic outlier handling is built into the model and
a dynamic stopping criterion based on the mean predicted covariance is
proposed. A measurement time reduction of about 70-90% was observed while still
ensuring an accuracy of above 90%.
