arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1809.08221cond-mat.str-elcond-mat.dis-nnphysics.comp-ph
收藏

应变石墨烯Bloch振荡的神经网络模拟研究

Study of simulated Bloch oscillations in strained graphene using neural networks

J. A. González, C. E. López, A. Raya

摘要:

我们考虑一个单层的石墨烯单轴拉伸应变和模拟布洛赫振荡不同的电场方向平行于单层的平面使用几个值的组件的均匀应变张量,但保持泊松比的范围内的可观察值。我们分析的轨迹的载流子与不同的初始条件,使用人工神经网络,训练分类的模拟信号,根据施加到膜的应变。当电场沿Z字形或扶手椅边缘取向时,我们的方法成功地分类了均匀应变张量的独立分量,准确度高达90%,预测值的误差为$\pm1\%$。对于任意方向的场,同时对应变张量分量和泊松比进行分类,获得高达97%的精度,其中应变张量分量的误差从$\pm5\%$到$\pm10\%$,泊松比的误差从$\pm12.5\%$到$\pm25\%$.

英文摘要:

We consider a monolayer of graphene under uniaxial, tensile strain and simulate Bloch oscillations for different electric field orientations parallel to the plane of the monolayer using several values of the components of the uniform strain tensor, but keeping the Poisson ratio in the range of observable values. We analyze the trajectories of the charge carriers with different initial conditions using an artificial neural network, trained to classify the simulated signals according to the strain applied to the membrane. When the electric field is oriented either along the Zig-Zag or the Armchair edges, our approach successfully classifies the independent component of the uniform strain tensor with up to 90\% of accuracy and an error of $\pm1\%$ in the predicted value. For an arbitrary orientation of the field, the classification is made over the strain tensor component and the Poisson ratio simultaneously, obtaining up to 97\% of accuracy with an error that goes from $\pm5\%$ to $\pm10\%$ in the strain tensor component and an error from $\pm12.5\%$ to $\pm25\%$ in the Poisson ratio.

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。

↑