Machine learning and first-principles guided design of BaTiO3-based materials for capacitor applications

Addressing the requirements of advanced capacitor applications for high dielectric permittivity, low loss, and strong frequency stability necessitates the accelerated development of materials exhibiting weakly coupled relaxor ferroelectric characteristics and broad temperature stability. BaTiO3 systems have attracted considerable interest owing to their high dielectric constant and tunable properties. However, conventional trial-and-error approaches and complex doping strategies hinder rapid progress. In this study, a data-driven approach combining first-principles calculations with machine learning (ML) was employed to predict the variation trends in formation energies for 30,301 Sr, La co-doped BaTiO3 compositions. The ferroelectric properties, crystal structures, elastic anisotropy, and thermal properties were systematically investigated at four representative doping levels (0, 0.125, 0.25, and 0.375) to elucidate the microscopic mechanism responsible for the emergence of weakly coupled relaxor ferroelectric behavior and to enable rapid identification of doping ranges that yield both relaxor ferroelectric characteristics and broad temperature stability. The results show that Sr, La co-doping in the range of 0.125-0.25 introduces compositional and displacement disorder that progressively suppresses long-range ferroelectric order, promotes the formation of polar nanoregions (PNRs), and drives the system toward a weakly coupled, relaxor-like dielectric response with reduced hysteretic loss, with the composition at 0.25 exhibiting enhanced mechanical and thermodynamic performance. These findings provide guidance for BaTiO3-based materials for high-frequency capacitor applications and offer a transferable framework for accelerating the study of doped material properties.