Group members published "Enhancing Thermoelectric Properties of Isotope Graphene Nanoribbons via Machine Learning Guided Manipulation of Disordered Antidots and Interfaces" in the International Journal of Heat and Mass Transfer. This work combines machine learning and atomic Green's function device transport analysis to achieve efficient and optimal design of the thermoelectric properties of graphene nanoribbons by manipulating the interfacial arrangement of isotopes and inverse points.

The first author of the paper is Huang Xiang, a Ph.D. student from the LCC of Shanghai Jiao Tong University, and the corresponding author is Associate Professor Ju Shenghong.

Original article link: https://doi.org/10.1016/j.ijheatmasstransfer.2022.123332