ABSTRACT Lattice strain is a key parameter governing the ferroelectric functionality of BaTiO 3 (BTO) thin films; however its precise evaluation typically requires extensive structural and electrical characterizations. Here, we introduce a machine‐learning framework that extracts strain information embedded in the surface morphology of epitaxial BTO thin films. By systematically correlating atomic force microscopy‐based surface morphology images with strain‐dependent structural and ferroelectric properties, including lattice parameters, crystalline coherence, and ferroelectric imprint, across thickness‐controlled BTO films, we construct a data‐driven model that infers lattice strain directly from surface morphology. The resulting framework accurately identifies strain states and classifies strain‐engineered functional regimes without relying on conventional diffraction‐based characterization. Notably, a model trained exclusively on BTO films on SrTiO 3 (001) substrates successfully generalizes to BTO films grown on LaAlO 3 (001), despite the distinct lattice mismatch and epitaxial environment. This result demonstrates that morphology‐encoded strain signatures persist beyond a specific epitaxy platform. Therefore, our approach establishes surface morphology as a powerful, non‐destructive descriptor for probing lattice strain and provides a scalable, data‐driven pathway for exploring strain‐engineered ferroelectric and emergent quantum functionalities in complex oxide thin films.
Yeom et al. (Fri,) studied this question.