MIT researchers have developed a machine-learning framework that predicts phonon dispersion relations in materials up to 1,000 times faster than other AI techniques, significantly speeding up the design of efficient energy-conversion systems and faster microelectronic devices. The method overcomes the complexities of modeling how heat is carried through materials by phonons, which can save time and advance material sciences.