In our day-to-day lives, advances in lightweight and flexible photovoltaics will promote a new generation of soft electronics and machines requiring high power-per-weight. Ultrathin flexible perovskite solar cells (F-PSCs) with high power-per-weight have displayed a unique potential for specific applications where lower weight, higher flexibility, and conformability are indispensable. This Review highlights the recent progress and practical applications of ultrathin and lightweight F-PSCs and demonstrates the routes toward enhanced device efficiency and improved mechanical and environmental stability concerning the choice of flexible substrates and the development of high-performance functional layers and flexible transparent electrodes. The fabrication technologies for mass production of efficient F-PSCs at large scale are then summarized, including continuous roll-to-roll methods integrated with low-temperature process. Furthermore, the practical applications focused on self-powered wearable electronic devices, solar-powered miniature unmanned aerial vehicles, and even solar modules operating in near-space are elaborated. Finally, the current challenging issues and future perspective are discussed, aiming to promote more extensive applications and commercialization processes for lightweight F-PSCs.

The electrochemical interface, where the adsorption of reactants and electrocatalytic reactions take place, has long been a focus of attention. Some of the important processes on it tend to possess relatively slow kinetic characteristics, which are usually beyond the scope of ab initio molecular dynamics. The newly emerging technique, machine learning methods, provides an alternative approach to achieve thousands of atoms and nanosecond time scale while ensuring precision and efficiency. In this Perspective, we summarize in detail the recent progress and achievements made by the introduction of machine learning to simulate electrochemical interfaces, and focus on the limitations of current machine learning models, such as accurate descriptions of long-range electrostatic interactions and the kinetics of the electrochemical reactions occurring at the interface. Finally, we further point out the future directions for machine learning to expand in the field of electrochemical interfaces.

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