  Machine learning applications in materials science are often hampered by
shortage of experimental data. Integration with legacy data from past
experiments is a viable way to solve the problem, but complex calibration is
often necessary to use the data obtained under different conditions. In this
paper, we present a novel calibration-free strategy to enhance the performance
of Bayesian optimization with preference learning. The entire learning process
is solely based on pairwise comparison of quantities (i.e., higher or lower) in
the same dataset, and experimental design can be done without comparing
quantities in different datasets. We demonstrate that Bayesian optimization is
significantly enhanced via addition of legacy data for organic molecules and
inorganic solid-state materials.
