  HfO2-based ferroelectric materials are promising for the next generation of
memory devices, attracting significant attention. However, their potential
applications are significantly limited by fatigue and imprint phenomena, which
affect device lifetime and memory capabilities. Here, to accurately describe
the dynamics and field effects of HfO2, we adopt our newly developed
DREAM-Allegro network scheme and develop a comprehensive machine-learning model
for HfO2. Such model can not only predict the interatomic potential, but also
predict Born effective charges. Applying such model, we explore the role of
domain dynamics in HfO2 and find that the fatigue and imprint phenomena are
closely related to the so-called E-path and T-path switching pathways. Based on
the different atomic motions in the two paths, we propose that an inclined
electric field can sufficiently suppress fatigue and enhancing the performance
of HfO2-based ferroelectric devices.
