论文标题
通过神经进化的基于石墨烯的量子传感器的逆设计
Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution
论文作者
论文摘要
我们引入了一个基于人工神经网络，遗传算法和紧密结合计算的逆设计框架，能够优化纳米电子设备的非常大的配置空间。我们的非线性优化程序通过控制着独特兴奋剂区域的增长政策的超级诉讼者对哈密顿量的试验进行操作。我们证明，我们的算法优化了用于谷LeTronics应用的基于石墨烯的三端设备的掺杂，单调地融合了几千个评估中具有高功绩功能的可综合设备（在$ \ simeq 2^{3800}中，可能的配置）。允许使用$ \ simeq 96 $ \％（$ \ simeq 94 \％）$ $ $ $ $ $ K $（$ k'$）valley palley pulity purity的终端特定设备，以特定于终端的山谷电流分离。重要的是，通过我们的非线性优化程序找到的设备既具有比通过几何优化获得的设备具有更高的优点函数，又具有更高的鲁棒性。
We introduce an inverse design framework based on artificial neural networks, genetic algorithms, and tight-binding calculations, capable to optimize the very large configuration space of nanoelectronic devices. Our non-linear optimization procedure operates on trial Hamiltonians through superoperators controlling growth policies of regions of distinct doping. We demonstrate that our algorithm optimizes the doping of graphene-based three-terminal devices for valleytronics applications, monotonously converging to synthesizable devices with high merit functions in a few thousand evaluations (out of $\simeq 2^{3800}$ possible configurations). The best-performing device allowed for a terminal-specific separation of valley currents with $\simeq 96$\% ($\simeq 94\%)$ $K$ ($K'$) valley purity. Importantly, the devices found through our non-linear optimization procedure have both higher merit function and higher robustness to defects than the ones obtained through geometry optimization.
