A new machine-learning framework could revolutionize the efficiency of energy generation systems by predicting heat movement through semiconductors and insulators with unprecedented speed and accuracy.
Solving this problem, however, hinges on understanding the thermal properties of materials, a complex task due to the behavior of phonons—the subatomic particles that carry heat. The phonon dispersion relation , which describes the relationship between energy and momentum of phonons within a material’s crystal structure, is especially difficult to model.
“Phonons are the culprit for thermal loss, yet obtaining their properties is notoriously challenging, either computationally or experimentally,” Mingda Li, an associate professor of nuclear science and engineering at MIT and the senior author of the paper, said in anHeat-carrying phonons are difficult to predict due to their wide frequency range and variable travel speeds.
The VGNN can rapidly estimate phonon dispersion relations and offers slightly greater accuracy in predicting a material’s heat capacity, the study argued. This efficiency allows for the calculation of phonon dispersion relations for thousands of materials within seconds on a personal computer, potentially accelerating the discovery of materials with superior thermal properties.