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arXiv 2603.07585cond-mat.mes-hall
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使用微分进化算法优化多障碍物石墨烯系统的量子传输

Optimizing quantum transport in multi-barrier graphene systems using differential evolution

School of Physical Sciences, Dublin City University(都柏林城市大学物理科学学院)

Leon Browne, Stephen R. Power

AI总结:

本文利用微分进化算法优化多障碍物石墨烯系统的量子传输,通过转移矩阵方法和正则化技术实现高精度传输控制。

摘要:

石墨烯中的势垒和质量势垒引入了电子散射,调节传输概率。复杂的多障碍物设置允许通过高精度控制电子传输,但具有巨大的可能势垒几何结构的设计空间。本文提出了一种框架,利用微分进化算法优化此类系统中的电子传输。首先,使用转移矩阵方法高效计算多障碍物结构中的量子传输,然后应用优化以找到传输特性接近预定义目标特性的障碍物配置。为了解决由此产生的配置精度与复杂性之间的权衡,将正则化技术纳入优化过程。我们的方法展示了通过进化启发式优化技术,在石墨烯基系统中实现高度可调电子传输的潜力。

英文摘要:

Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multi-barrier setups allow electron transmission to be controlled with high precision, but have a huge design space of possible barrier geometries. This work presents a framework to optimize electronic transport in such systems using differential evolution algorithms. First, transfer matrix methods are employed to efficiently compute quantum transport through multi-barrier structures, before optimization is applied to find barrier configurations whose transmission profiles closely match a predefined target profile. To address the trade-off between the accuracy and complexity of resulting barrier configurations, regularization techniques are incorporated into the optimization process. Our approach demonstrates the potential for highly tunable electronic transport in graphene-based systems by exploiting evolution-inspired optimization techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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