标题： 基于机器学习的二维材料中激子效应的洞察 显示英文标题

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

摘要： 理解二维（2D）材料中的激子效应对于推动其在下一代电子和光子器件中的潜力至关重要。 在本研究中，我们引入了一种基于机器学习（ML）的框架，用于预测二维材料中的激子结合能，为传统方法如多体微扰理论（GW）和Bethe-Salpeter方程提供了计算效率更高的替代方案。 利用来自计算二维材料数据库（C2DB）的数据，我们的ML模型建立了廉价可获得的材料描述符与复杂的激子性质之间的联系，显著加速了具有显著激子效应材料的筛选过程。 此外，采用了带有高斯过程回归的贝叶斯优化来高效筛选具有最大激子结合能的材料，进一步提高了发现过程的效率。 尽管该方法是为二维系统开发的，但其具有通用性，可以扩展到三维材料，从而扩大了其在材料发现中的适用性。 显示英文摘要

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.