Polymer dielectrics with excellent processability and high breakdown strength (Eb) enable the development of high‐energy‐density capacitors. Although the improvement of dielectric constant (K) of polymer dielectric has been realized by adding high‐K inorganic fillers with high contents (>10 vol%), this approach faces significant challenges in scalable film processing. Here, the incorporation of ultralow ratios (<1 vol%) of low‐K Cd1−xZnxSe1−ySy nanodots into a ferroelectric polymer is reported. The polymer composites exhibit substantial and concurrent increase in both K and Eb, yielding a discharged energy density of 26.0 J cm−3, outperforming the current dielectric polymers and nanocomposites measured at ≤600 MV m−1. The observed unconventional dielectric enhancement is attributed to the structural changes induced by the nanodot fillers, including transformation of polymer chain conformation and induced interfacial dipoles, which have been confirmed by density function theory calculations. The dielectric model established in this work addresses the limitations of the current volume‐average models on the polymer composites with low filler contents and gives excellent agreement to the experimental results. This work provides a new experimental route to scalable high‐energy‐density polymer dielectrics and also advances the fundamental understanding of the dielectric behavior of polymer nanocomposites at atomistic scales.

A progressive learning method with an instrumental variable and bond-valence vector sums was used to improve the bandgap prediction precision.

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There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users' privacy. Here, the authors propose a face template protection technique by using multi-label learning, which maps the facials into low-density parity-check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi-label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra-variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

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