High-Temperature Polymer Dielectrics Designed Using an Invertible Molecular Graph Generative Model.

Generating new molecules with the desired physical or chemical properties is the key challenge of computational material design. Deep learning techniques are being actively applied in the field of data-driven material informatics and provide a promising way to accelerate the discovery of innovative materials. In this work, we utilize an invertible graph generative model to generate hypothetical promising high-temperature polymer dielectrics. A molecular graph generative model based on the invertible normalizing flow is trained on a data set containing 250k polymer molecular graphs (mostly generated by an RNN-based generative model) to learn the invertible transformations between latent distributions and molecular graph structures. When generating molecular graphs, a sample vector is drawn from the latent space, and then an adjacency tensor and node attribute matrix are generated through two invertible flows in two steps and assembled into a molecular graph. The model has the merits of exact likelihood training and an efficient one-shot generation process. The learned latent space is used to generate polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) for the application of high-temperature energy storage film capacitors. This work contributes to the efficient design of high-temperature polymer dielectrics by using deep generative models.

Recent Progress and Future Prospects on All-Organic Polymer Dielectrics for Energy Storage Capacitors.

With the development of advanced electronic devices and electric power systems, polymer-based dielectric film capacitors with high energy storage capability have become particularly important. Compared with polymer nanocomposites with widespread attention, all-organic polymers are fundamental and have been proven to be more effective choices in the process of scalable, continuous, and large-scale industrial production, leading to many dielectric and energy storage applications. In the past decade, efforts have intensified in this field with great progress in newly discovered dielectric polymers, fundamental production technologies, and extension toward emerging computational strategies. This review summarizes the recent progress in the field of energy storage based on conventional as well as heat-resistant all-organic polymer materials with the focus on strategies to enhance the dielectric properties and energy storage performances. The key parameters of all-organic polymers, such as dielectric constant, dielectric loss, breakdown strength, energy density, and charge-discharge efficiency, have been thoroughly studied. In addition, the applications of computer-aided calculation including density functional theory, machine learning, and materials genome in rational design and performance prediction of polymer dielectrics are reviewed in detail. Based on a comprehensive understanding of recent developments, guidelines and prospects for the future development of all-organic polymer materials with dielectric and energy storage applications are proposed.