AI method radically speeds predictions of materials’ thermal properties

It is estimated that about 70 percent of the energy generated worldwide ends up as waste heat.If scientists could better predict how heat moves through semiconductors and insulators, they could design more efficient power generation systems. However, the thermal properties of materials can be exceedingly difficult to model.The trouble comes from phonons, which are subatomic particles that carry heat. Some of a material’s thermal properties depend on a measurement called the phonon dispersion relation, which can be incredibly hard to obtain, let alone utilize in the design of a system.A…

This content is for Member members only.
Log In Register