  Autonomous experimentation (AE) combines machine learning and research
hardware automation in a closed loop, guiding subsequent experiments toward
user goals. As applied to materials research, AE can accelerate materials
exploration, reducing time and cost compared to traditional Edisonian studies.
Additionally, integrating knowledge from diverse sources including theory,
simulations, literature, and domain experts can boost AE performance. Domain
experts may provide unique knowledge addressing tasks that are difficult to
automate. Here, we present a set of methods for integrating human input into an
autonomous materials exploration campaign for composition-structure phase
mapping. The methods are demonstrated on x-ray diffraction data collected from
a thin film ternary combinatorial library. At any point during the campaign,
the user can choose to provide input by indicating regions-of-interest, likely
phase regions, and likely phase boundaries based on their prior knowledge
(e.g., knowledge of the phase map of a similar material system), along with
quantifying their certainty. The human input is integrated by defining a set of
probabilistic priors over the phase map. Algorithm output is a probabilistic
distribution over potential phase maps, given the data, model, and human input.
We demonstrate a significant improvement in phase mapping performance given
appropriate human input.
