The extensive employment of nitroaromatic pollutants has led to severe contamination of the environmental waters. Here, we proposed low-cost single-component WO3 microrobots capable of efficient photodegradation of nitroaromatic pollutants in a sustainable way.

The prediction of properties of molecules and materials requires both quantum-chemical accuracy combined with large length- and time-scales. We proposed a deep learning approach that overcomes the limitations of existing methods and provides a step towards increased predictive power of simulation.