Advanced deep learning framework for piezoelectric characterization of organic polymeric materials

One example of this kind of energy conversion is the piezoelectric effect, which may be defined as the transformation of mechanical energy into electrical energy and then back again. Several organic compounds exhibit piezoelectric properties, allowing polymeric materials to generate an electric charge when mechanical stress or strain is applied. This capability is widely used in sensing, actuation, and energy-harvesting systems. In organic polymers, the piezoelectric effect arises from molecular dipole alignment, influenced by polymer type, processing conditions, and filler content. Due to their high piezoelectricity, biocompatibility, biodegradation, good mechanical strength, and low cost of fabrication, organic piezoelectric materials have received much focus in biomedical uses, especially in forming implantable devices. Deep learning is also used to improve piezoelectric technologies, and it can predict the properties of materials, architecture optimization, and the mechanical-electrical behavior of polymer materials. This method aids in the design of efficient and tougher piezoelectric harvesters, sensors and actuators. Here, the paper describes the construction of an artificial piezoelectric crystal with an impressive degree of mechanical flexibility and the capability of being recycled. Inspired by the geometrical property of realized piezoelectric crystals, the work uses LSTM and GRU models with Bayesian optimization to optimize material performance and device architecture and facilitate the application of organic piezoelectric materials.