AI method rapidly speeds predictions of materials' thermal properties

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.

• 70 percent of energy ends up as waste heat.
• Phonons are subatomic particles that carry heat.
• VGNN method can be 1 million times faster than non-AI approaches.
• The research was published in Nature Computational Science.
• Technique could improve energy generation and electronics.