Matter that learns: A closed-AI-loop journey in energetic materials

This is a perspective on how energetic materials can learn—and teach. What began as a search for high-energy-density structure and high stability has grown into a dialogue among matter, models, and machines. Along this path, the ideas of dual aromaticity, multiscale thinking, and intelligent design converged into a single loop: letting materials guide their own discovery. This piece reflects on that journey and argues for a more reciprocal relationship between science and matter itself.