Data-driven design of carbon-based materials for high-performance flexible energy storage devices

With the rise of flexible electronics, the demand for advanced power sources has grown. Developing high-performance energy storage devices requires comprehensive consideration of various factors such as elec-trodes, electrolytes, and service conditions. Herein, a data-driven research framework is proposed to optimize the electrode-electrolyte system in supercapacitors. With the help of machine learning, we reveal the key factors affecting the capacitance performance of carbon-based materials. According to the algorithm analysis, a kind of 3D carbon network is prepared with controlled composition and structure, which is incorporated with a high -safety ionic liquid to obtain a supercapacitor device. This device with high energy density and impressive flexibility can maintain operational stability under extreme conditions such as humidity, shock, and localized damage. Overall, this work presents a typical pipeline for accelerating the design of energy-related devices.

Automated summary

With the rise of flexible electronics, the demand for advanced power sources has grown. This work proposes a data-driven research framework to optimize the electrode-electrolyte system in supercapacitors, using machine learning to reveal key factors affecting the capacitance performance. A 3D carbon network with controlled composition and structure is prepared, combined with a high-safety ionic liquid to obtain a supercapacitor device. This device exhibits high energy density and impressive flexibility, maintaining operational stability under extreme conditions. Overall, this work presents a typical pipeline for accelerating the design of energy-related devices.