High Mechanical Energy Storage Capacity of Ultranarrow Carbon Nanowires Bundles by Machine Learning Driving Predictions

作者：Luneng Zhao, Yuan Chang, Shi Qiu, Hongsheng Liu, Jijun Zhao, Junfeng Gao · 发表于：Advanced Energy and Sustainability Research · 年份：2023 · DOI：10.1002/aesr.202300112 · 被引用次数：14 · 研究领域：Machine Learning in Materials Science、Carbon Nanotubes in Composites、Supercapacitor Materials and Fabrication

Energy storage and renewable energy sources are critical for addressing the growing global energy demand and reducing the negative environmental impacts of fossil fuels. Carbon nanomaterials are extensively explored as high reliable, reusable, and high‐density mechanical energy storage materials. In this context, machine learning techniques, specifically machine learning potentials (MLPs), are employed to explore the elastic properties of 1D carbon nanowires (CNWs) as a promising candidate for mechanical energy storage applications. The study focuses on the elastic energy storage properties of these CNWs, utilizing MLPs trained with data from first‐principles molecular dynamics simulations. It is found that these materials exhibit an exceptionally high tensile elastic energy storage capacity, with a maximum storage density ranging from 2262 to 2680 kJ kg −1 . Furthermore, it is discovered that some CNWs exhibit a superior torsional energy storage capacity compared to their tensile energy storage capacity. Overall, this research demonstrates the effectiveness of machine learning‐based computational approaches in accelerating the exploration and optimization of novel materials. It also highlights the potential of CNWs as promising candidates for future energy storage applications.