Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency

Abstract

The gamma-graphyne nanoribbons (γ-GYNRs) incorporating diamond-shaped segment (DSSs) with excellent thermoelectric properties are systematically investigated by combining nonequilibrium Green’s functions with adaptive genetic algorithm. Our calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric performance. In multiple rounds, an average of 476 candidates (only 2.88% of all 16512 candidate structures) are calculated to obtain the structures with extremely high thermoelectric conversion efficiency. The room temperature thermoelectric figure of merit (ZT) of the optimal γ-GYNR incorporating DSSs is 1.622, which is about 5.4 times higher than that of pristine γ-GYNR (length 23.693 nm and width 2.660 nm). The significant improvement of thermoelectric performance of the optimal γ-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance (proactive effect) and reduction of thermal power factor (side effect). Moreover, through exploration of the main variables affecting the genetic algorithm, it is revealed that the efficiency of the genetic algorithm can be improved by optimizing the initial population gene pool, selecting a higher individual retention rate and a lower mutation rate. The results presented in this paper validate the effectiveness of genetic algorithm in accelerating the exploration of γ-GYNRs with high thermoelectric conversion efficiency, and could provide a new development solution for carbon-based thermoelectric materials.