arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.03839cond-mat.mtrl-sci
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扭曲双层石墨烯拉曼光谱分析的机器学习模型

Machine-learning models for Raman spectra analysis of twisted bilayer graphene

Natalya Sheremetyeva, Michael Lamparski, Colin Daniels, Benoit Van Troeye, Vincent Meunier

摘要:

扭曲双层石墨烯(tBLG)的振动特性表现出复杂的功能,由于其低对称性配置的复杂的能量景观。一个基于机器学习的方法被开发,以提供一个连续的模型之间的扭曲角和tBLG的模拟拉曼光谱。从拉曼光谱中提取扭转角的结构信息相当于求解一个复杂的逆问题。一旦经过训练,机器学习回归器(MLR)可以快速提供预测,而不会产生人为偏见,并且模型可以解释平均98%的数据方差。的显着的光谱特征学习的MLRs进行了分析,揭示了计算的G带附近的强度分布是最重要的功能。训练的模型进行测试,含噪声的测试数据,证明其鲁棒性。本模型的实验拉曼光谱的可转移性进行了讨论的背景下,用于构建分析数据库的理论水平的验证。这项工作证明了机器学习分析是解释tBLG和其他2D材料拉曼光谱的潜在强大工具。

英文摘要:

The vibrational properties of twisted bilayer graphene (tBLG) show complex features, due to the intricate energy landscape of its low-symmetry configurations. A machine learning-based approach is developed to provide a continuous model between the twist angle and the simulated Raman spectra of tBLGs. Extracting the structural information of the twist angle from Raman spectra corresponds to solving a complicated inverse problem. Once trained, the machine learning regressors (MLRs) quickly provide predictions without human bias and with an average of 98% of the data variance being explained by the model. The significant spectral features learned by MLRs are analyzed revealing the intensity profile near the calculated G-band to be the most important feature. The trained models are tested on noise-containing test data demonstrating their robustness. The transferability of the present models to experimental Raman spectra is discussed in the context of validation of the level of theory used for the construction of the analyzed database. This work serves as a proof of concept that machine-learning analysis is a potentially powerful tool for interpretation of Raman spectra of tBLG and other 2D materials.

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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