Anomalously High Apparent Young's Modulus of Ultrathin Freestanding PZT Films Revealed by Machine Learning Empowered Nanoindentation.

The mechanical properties at small length scales are not only significant for understanding the intriguing size-dependent behaviors but also critical for device applications. Nanoindentation via atomic force microscopy is widely used for small-scale mechanical testing, yet determining the Young's modulus of quasi-2D films from freestanding force-displacement curve of nanoindentation remains challenging, complicated by both bending and stretching that are highly nonlinear. To overcome these difficulties, a machine learning model is developed based on the back propagation (BP) neural network and finite element training to accurately determine the Young's modulus, pretension, and thickness of freestanding films from nanoindentation force-displacement curves simultaneously, improving the computational efficiency by two orders of magnitude over conventional brute force curve fitting.

Enhanced Energy Storage Performance by A-B Site Ambipolar Co-Doping in Antiferroelectrics.

Antiferroelectric materials are promising to be used for power capacitive devices. To improve the energy storage performance, solid-solution and defect engineering are widely used to suppress the long-range order by introducing local heterogeneities. However, both methods generally deteriorate either the maximum polarization or breakdown electric field due to damaged intrinsic polarization or increased leakage.