标题： 通过量子输运和机器学习分子动力学模拟优化石墨烯抗点阵的热电性能 显示英文标题

标题： Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations

摘要： 热电材料能够将废热转化为电能，或者用作固态冷却器，在可持续能源应用方面具有广阔的前景。然而，由于电子和热输运性质之间的复杂相互作用，优化热电性能仍然是一个重大挑战。在这项工作中，我们系统地优化了石墨烯抗点阵（GALs）中的 $ZT$，这是一种具有周期性纳米孔的纳米结构石墨烯片，其特征由两个几何参数决定：六边形单元的边长 $L$ 和抗点半径 $R$。晶格热导率通过基于机器学习势驱动的分子动力学（MD）模拟确定，而电子输运性质则利用基于键长相关紧束缚模型的线性标度量子输运方法结合基于 MD 轨迹进行计算。该方法能够考虑电子-声子散射，允许在大规模系统中研究扩散输运，克服了以往基于非平衡格林函数形式主义方法的局限性。我们的结果显示，引入抗点有效地解耦了晶格和电子输运，并导致了温特姆-弗兰兹定律的有利且显著的违反。我们发现，在具有中间值 $L$ 和 $R$ 的 GALs 中，$ZT$ 的最佳值与峰值功率因子值密切相关。值得注意的是，热电性能在接近室温时达到峰值，最大 $ZT$ 值接近 2，这表明 GALs 是高性能热电能量转换的理想候选材料。 显示英文摘要

摘要： Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing thermoelectric performance remains a significant challenge due to the complex interplay between electronic and thermal transport properties. In this work, we systematically optimize $ZT$ in graphene antidot lattices (GALs), nanostructured graphene sheets with periodic nanopores characterized by two geometric parameters: the hexagonal unit cell side length $L$ and the antidot radius $R$. The lattice thermal conductivity is determined through machine-learned potential-driven molecular dynamics (MD) simulations, while electronic transport properties are computed using linear-scaling quantum transport in combination with MD trajectories based on a bond-length-dependent tight-binding model. This method is able to account for electron-phonon scattering, allowing access to diffusive transport in large-scale systems, overcoming limitations of previous methods based on nonequilibrium Green function formalism. Our results show that the introduction of the antidots effectively decouples lattice and electronic transport and lead to a favorable and significant violation of the Wiedemann-Franz law. We find that optimal $ZT$ values occur in GALs with intermediate $L$ and $R$, closely correlated with peak power factor values. Notably, thermoelectric performance peaks near room temperature, with maximal $ZT$ values approaching 2, highlighting GALs as promising candidates for high-performance thermoelectric energy conversion.