  The fusion of experimental automation and machine learning has catalyzed a
new era in materials research, prominently featuring Gaussian Process Bayesian
Optimization (GPBO) driven autonomous experiments navigating complex
experimental conditions for accelerated scientific discovery. In traditional
GPBO-driven experiments, a predefined scalarizer function is often required to
preprocess the experimental data, transforming non-scalar raw data into scalar
descriptors for GP training. However, such predefined scalarizer functions have
limitations, which likely fail to accommodate the diversity and complexity of
real-world experimental data, potentially skewing experimental outcomes. Thus,
oversight and quality control are necessitated over the process to avoid GPBO
from being misled by low quality scalarizers. To address the limitation, we
introduce a Dual-GP approach that enhances traditional GPBO by adding a
secondary surrogate model to dynamically constrain the experimental space based
on real-time assessments of the raw experimental data. This Dual-GP approach
enhances the optimization efficiency of traditional GPBO by isolating more
promising space for BO sampling and more valuable experimental data for primary
GP training. We also incorporate a flexible, human-in-the-loop intervention
method in the Dual-GP workflow to adjust for unanticipated results. We
demonstrate the effectiveness of the Dual-GP model with synthetic model data
and implement this approach in autonomous pulsed laser deposition experimental
data. This Dual-GP approach has broad applicability in diverse GPBO-driven
experimental settings, providing a more adaptable and precise framework for
refining autonomous experimentation for more efficient optimization.
