Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

TL;DR

This paper introduces a multi-objective Bayesian optimization workflow tailored for ferroelectric materials, enabling efficient exploration of parameter space to optimize performance for memory and energy storage applications.

Contribution

The work presents a novel physics-driven MOBO architecture that efficiently builds Pareto-frontiers for ferroelectric materials, integrating physical models with Bayesian optimization.

Findings

Developed a physics-driven decision tree for target functions.

Built Pareto-frontiers for energy storage and loss optimization.

Demonstrated rapid initial parameter selection for materials and devices.

Abstract

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).