基于贝叶斯算法的5-7环缺陷石墨烯纳米带热电性能优化设计

Optimal design of thermoelectric properties of graphene nanoribbons with 5-7 ring defects based on Bayesian algorithm

由于结构巨大的自由度,缺陷石墨烯纳米带热电转换性能的优化设计是材料研究领域的难点之一.本文利用非平衡格林函数结合贝叶斯算法,对5-7环缺陷石墨烯纳米带热电性能进行了优化设计.研究结果表明,在搜寻具有高热电转换效率5-7环缺陷石墨烯纳米带的过程中,贝叶斯算法具备有效性和优越性.计算发现,利用贝叶斯算法能快速且准确地从32896个候选结构中搜索到具有最佳热电转换性能的唯一构型.即使在效率最低的一轮优化中,也仅需要计算1495个候选结构(约占所有候选结构的4.54%)即可寻找到最佳构型.研究还发现,在室温下的最佳构型5-7环缺陷石墨烯纳米带(长和宽分别为21.162nm和1.23 nm)的热电优值ZT (约1.13)较完美石墨烯纳米带(约0.14)提升了近一个量级.这主要归因于5-7环缺陷有效抑制了系统的电子热导率,使得功率因子的减弱作用和热导率的抑制作用(正效应)之间达到了最大平衡.研究结果为设计和制备具有优异热电转换效率的石墨烯纳米带热电器件提供了一种新的可行性方案.

Owing to the huge degree of freedom of structure,the optimal design of thermoelectric conversion performance of defective graphene nanoribbons is one of the difficulties in the field of materials research.In this paper,the thermoelectric properties of graphene nanoribbons with 5-7 ring defects are optimized by using nonequilibrium Green’s function combined with Bayesian algorithm.The results show that the Bayesian algorithm is effective and advantageous in the search of graphene nanoribbons with 5-7 ring defects with high thermoelectric conversion efficiency.It is found that the single configuration with the best thermoelectric conversion performance can be quickly and accurately searched from 32896 candidate structures by using Bayesian algorithm.Even in the least efficient round of optimization,only 1495 candidate structures(about 4.54% of all candidate structures) need to be calculated to find the best configuration.It is also found that the thermoelectric value ZT(about 1.13) of the optimal configuration of 5-7 ring defective graphene nanoribbons(21.162 and 1.23 nm in length and width,respectively) at room temperature is nearly one order of magnitude higher than that of the perfect graphene nanoribbons(about 0.14).This is mainly due to the fact that the 5-7ring defects effectively inhibit the electron thermal conductivity of the system,which makes the maximum balance between the weakening effect of the power factor and the inhibiting effect of the thermal conductivity(positive effect).The results of this study provide a new feasible scheme for designing and fabricating the graphene nanoribbon thermoelectric devices with excellent thermoelectric conversion efficiencies.