Ming Hu

Artificial intelligence and the frontier of phonon engineering: a perspective on discovering extreme thermal materials

This Perspective traces the evolution of thermal transport science from its empirical origins through the current AI-driven renaissance, and examines the limitations of conventional density-functional-theory-based phonon workflows, the emergence of universal machine learning interatomic potentials (uMLPs), graph-neural-network screening architectures such as the Crystal Attention Graph Neural Network (CATGNN), and generative inverse-design frameworks.