Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials

Abstract

In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO₂ based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO₂. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO₂ materials, offering fresh insights into the design and performance prediction of HfO₂ ferroelectric thin films.