  Determining a process-structure-property relationship is the holy grail of
materials science, where both computational prediction in the forward direction
and materials design in the inverse direction are essential. Problems in
materials design are often considered in the context of process-property
linkage by bypassing the materials structure, or in the context of
structure-property linkage as in microstructure-sensitive design problems.
However, there is a lack of research effort in studying materials design
problems in the context of process-structure linkage, which has a great
implication in reverse engineering. In this work, given a target
microstructure, we propose an active learning high-throughput microstructure
calibration framework to derive a set of processing parameters, which can
produce an optimal microstructure that is statistically equivalent to the
target microstructure. The proposed framework is formulated as a noisy
multi-objective optimization problem, where each objective function measures a
deterministic or statistical difference of the same microstructure descriptor
between a candidate microstructure and a target microstructure. Furthermore, to
significantly reduce the physical waiting wall-time, we enable the
high-throughput feature of the microstructure calibration framework by adopting
an asynchronously parallel Bayesian optimization by exploiting high-performance
computing resources. Case studies in additive manufacturing and grain growth
are used to demonstrate the applicability of the proposed framework, where
kinetic Monte Carlo (kMC) simulation is used as a forward predictive model,
such that for a given target microstructure, the target processing parameters
that produced this microstructure are successfully recovered.
