Machine learning helps identify 'thermal switch' for next-generation nanomaterials
Imagine being able to program materials to control heat like you can control a light with a dimmer switch. By simply squeezing or stretching the materials, you can make them hotter or colder. One of the fundamental challenges in advanced materials has been accurately predicting and controlling heat flow in complex, next-generation materials. Traditional simulation methods, which rely on simplified empirical models, fail to capture a material's intricate atomic interactions, especially under deformation. New research by Xiangyu Li, an assistant professor in the Department of Mechanical and Aerospace Engineering, and his Ph.D. student, Shaodong Zhang, helps alleviate that problem.
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