Piezoelectric materials that can effectively convert natural mechanical energy into electrical energy without time and space constraints have been widely applied for energy harvesting and conversion. The piezocomposites with high piezoelectricity and flexibility have shown great promise for renewable electric energy generation that can power implantable and wearable electronics. This minireview aims to summarize the recent progress of the piezocomposites with different composite structures, as well as the role of the theoretical understandings and designs in the development of new piezoelectric nanogenerator materials. Thereinto, the most common composite structural types (0-3, 1-3, and 3-3) have been discussed systematically. Several strategies for high output performance of piezocomposites are also proposed on the basis of current experimental and simulation results. Finally, the review concludes with perspectives on the future design of flexible piezoelectric nanocomposites for energy harvesters.

ChatGPT has become a very popular artificial intelligence tool recently, with the ability to “chat” with human beings. It can perform some challenging tasks such as writing essays, coding, composing, painting, etc. While the roles of ChatGPT in helping with literature reviews and language editing have been widely debated, the potential applications of ChatGPT in computational materials science have yet to be discussed. Herein, we will briefly discuss 3 aspects that ChatGPT could potentially be applied for computational materials science, i.e., building structures, writing codes for specific scientific software, and preparing data visualization scripts. It is found that while ChatGPT can make some simple mistakes when trying to accomplish general tasks, it has the ability to learn from our words when interacting with us. Meanwhile, we should also pay attention to some problems such as the consistency of the output, hidden errors, and ethical concerns. We hope that this perspective will spur further interest in the potential applications of ChatGPT in computational materials science.

Surface energy is one of the most important physical and chemical properties for crystals, which has a significant impact on surface catalysis, surface adsorption, epitaxial growth, nucleation, and dendrite growth. Rapid calculation and prediction of crystal surface energies can favor accelerating the design and optimization of catalysis materials, battery materials, and alloys. In this paper, a data-driven machine learning algorithm was proposed with a crystal graph convolutional neural network framework for the prediction of metal surface energy from the crystal structure. Using a physics-based surface representation that couples the surface dimensions to the atomic and bonding features of the crystal, we obtained an MAE value of less than 0.002 eV/Ų, which surpasses other math-based surface models. Compared with the first-principles calculation, the computation time is reduced by approxiamtely 5 orders of magnitude. In addition, we discussed the main challenges and solutions towards the surface energy prediction of more complicated systems such as Silicates. It is expected that this work could be a paradigm for the surface energy prediction with machine learning.