  Optimization of materials performance for specific applications often
requires balancing multiple aspects of materials functionality. Even for the
cases where generative physical model of material behavior is known and
reliable, this often requires search over multidimensional parameter space to
identify low-dimensional manifold corresponding to required Pareto front. Here
we introduce the multi-objective Bayesian Optimization (MOBO) workflow for the
ferroelectric/anti-ferroelectric performance optimization for memory and energy
storage applications based on the numerical solution of the Ginzburg-Landau
equation with electrochemical or semiconducting boundary conditions. MOBO is a
low computational cost optimization tool for expensive multi-objective
functions, where we update posterior surrogate Gaussian process models from
prior evaluations, and then select future evaluations from maximizing an
acquisition function. Using the parameters for a prototype bulk
antiferroelectric (PbZrO3), we first develop a physics-driven decision tree of
target functions from the loop structures. We further develop a physics-driven
MOBO architecture to explore multidimensional parameter space and build
Pareto-frontiers by maximizing two target functions jointly: energy storage and
loss. This approach allows for rapid initial materials and device parameter
selection for a given application and can be further expanded towards the
active experiment setting. The associated notebooks provide both the tutorial
on MOBO and allow to reproduce the reported analyses and apply them to other
systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).
