A Li–S battery based on amorphous NiS2showed fabulous stability and high S utilization.

Machine learning (ML) has excellent potential in molecule property prediction and new molecule discovery. However, real-world synthesis is the most vital part of determining a polymer's value. This paper demonstrates...

Organic redox compounds are rich in elements and structural diversity, which are an ideal choice for lithium-ion batteries. However, most organic cathode materials show a trade-off between specific capacity and voltage, limiting energy density. By increasing the redox potential of cathode materials, the balance between redox potential and specific capacity can be broken to increase energy density. In this work, we use machine learning to train materials with different redox potentials to predict novel polymers with ideal potentials. In situ computer vision and infrared spectroscopy monitor the reaction in real time. We also theoretically studied the concentrationdependent yields by providing a depletion-force model. This work provides a new solution to material research flow, including training, prediction, synthesis, examination, and analysis, accelerating high-capacity organic cathode material discovery.