Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics

TL;DR

This study uses machine learning-enhanced molecular dynamics to understand and control phase boundaries in ferroelectric materials, revealing how composition influences microstructure and matching experimental observations.

Contribution

It introduces a machine-learning-based simulation framework that accurately predicts phase boundary behavior and microstructural evolution in solid-solution ferroelectrics.

Findings

Chemical composition modulates polymorphic phase boundaries.

Predicted diffused phase boundaries and nano regions match experiments.

Elastic and electrostatic mismatches drive microstructural evolution.

Abstract

Atomistic control of phase boundaries is crucial for optimizing the functional properties of solid-solution ferroelectrics, yet their microstructural mechanisms remain elusive. Here, we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO3-KTaO3 (KNTO) system. Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries (PPBs), offering a versatile pathway for materials engineering. Diffused PPBs and polar nano regions, predicted by our model, highly match with experiments, underscoring the fidelity of the machine-learning atomistic simulation. Crucially, we identify elastic and electrostatic mismatches between ferroelectric KNbO3 and paraelectric KTaO3 as the driving forces behind complex microstructural evolution. This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics.