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arXiv 2501.01092cond-mat.mtrl-sciphysics.comp-ph
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基于机器学习的二维材料中激子效应洞察

Machine Learning-Driven Insights into Excitonic Effects in 2D Materials

Ahsan Javed, Sajid Ali

AI总结:

本研究提出基于机器学习的框架,用于高效预测二维材料的激子结合能,加速材料筛选并拓展至三维材料应用。

摘要:

理解二维材料中的激子效应对于推进其在下一代电子和光子器件中的应用潜力至关重要。在本研究中,我们引入了一个基于机器学习(ML)的框架,用于预测二维材料中的激子结合能,提供了一种比传统方法如多体微扰理论(GW)和贝塞-萨尔佩特方程更具计算效率的替代方案。利用计算二维材料数据库(C2DB)中的数据,我们的ML模型建立了廉价可用的材料描述符与复杂激子性质之间的联系,显著加速了具有明显激子效应的材料筛选过程。此外,采用贝叶斯优化与高斯过程回归来高效筛选激子结合能最大的材料,进一步增强了发现过程。尽管该方法最初针对二维系统开发,但其方法具有通用性,可扩展至三维材料,扩大了其在材料发现中的应用范围。

英文摘要:

Understanding excitonic effects in two-dimensional (2D) materials is critical for advancing their potential in next-generation electronic and photonic devices. In this study, we introduce a machine learning (ML)-based framework to predict exciton binding energies in 2D materials, offering a computationally efficient alternative to traditional methods such as many-body perturbation theory (GW) and the Bethe-Salpeter equation. Leveraging data from the Computational 2D Materials Database (C2DB), our ML models establish connections between cheaply available material descriptors and complex excitonic properties, significantly accelerating the screening process for materials with pronounced excitonic effects. Additionally, Bayesian optimization with Gaussian process regression was employed to efficiently filter materials with largest exciton binding energies, further enhancing the discovery process. Although developed for 2D systems, this approach is versatile and can be extended to three-dimensional materials, broadening its applicability in materials discovery.

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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