发表机构
Centre for Materials Science and Nanotechnology, University of Oslo（奥斯陆大学材料科学与纳米技术中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发卷积神经网络框架，基于合成多极-Padé表示训练，可高效鲁棒地从复杂介电谱中高精度反演等离激元极点参数，替代传统非线性拟合，实现介电谱高通量自动化分析。
AI 中文摘要
从介电谱中提取能量位置、展宽和谱权重等光谱性质，对于解释多体物理中的集体电子激发至关重要。传统方法通常依赖非线性拟合程序，这些程序计算量大，且对初始化和拟合选择高度敏感。本研究中，我们开发了一种卷积神经网络（CNN）框架，用于通过多极-Padé表示将介电谱直接反演为其底层的等离激元极点结构。该网络并非在与特定材料集合相关的受限光谱数据库上训练，而是完全基于从解析多极-Padé表达式生成的具有随机参数的合成光谱进行训练。这使网络能够以无偏的方式学习光谱与其特征之间的通用映射，无需源自真实材料的大型、特定材料的数据集。我们证明，经合成训练的网络可泛化到复杂的第一性原理和实验介电谱，在单次前向传播中以高精度提取底层极点参数。该方法为传统非线性拟合提供了一种高效且鲁棒的替代方案，支持对不同材料和应用的介电谱进行高通量、自动化分析。
英文摘要
Extracting spectral properties such as energy position, broadening, and spectral weight from dielectric spectra is critical for interpreting collective electronic excitations in many-body physics. Conventional approaches typically rely on nonlinear fitting procedures that can be computationally demanding and highly sensitive to initialization and fitting choices. In this work, we develop a convolutional neural-network framework for the direct inversion of dielectric spectra into their underlying plasmonic pole structures using a multipole-Padé representation. Rather than training on a constrained database of spectra associated with a specific set of materials, the network is trained entirely on synthetic spectra generated from the analytical multipole-Padé expression with randomized parameters. This enables the network to learn the general mapping between the spectra and their features in an unbiased way, without requiring large, material-specific datasets derived from real materials. We demonstrate that the synthetically trained network generalizes to complex first-principles and experimental dielectric spectra, extracting the underlying pole parameters with high accuracy in a single forward pass. This approach provides an efficient and robust alternative to conventional nonlinear fitting, enabling high-throughput, automated analysis of dielectric spectra across diverse materials and applications.
