Michele Ceriotti unites rigorous statistical mechanics with machine learning to map structure–property relations from first principles. He introduced widely adopted descriptors and kernel methods (e.g., SOAP) and champions uncertainty-aware, active-learning workflows and open-source tools (such as i-PI and rascal/rascaline).