Speaker
Description
Spectroscopic characterization of quantum and low-dimensional materials remains a fundamental challenge in condensed matter physics, especially when data are noisy or scarce. In this work, we explore a general deep learning framework for the automated classification and structural identification of two-dimensional (2D) materials from Raman spectra. Our approach requires no manual feature engineering and remains robust under severe signal degradation, enabling accurate twist-angle identification in bilayer graphene and similar systems. We emphasize the method’s physics relevance: it can extract latent structural information typically accessible only through time-consuming manual preprocessing or high-resolution techniques. The framework also lends itself to generalization across domains—offering a blueprint for integrating generative modeling and representation learning in other spectroscopy-based fields. This work represents a concrete step toward physics-aware, noise-resilient machine learning and may open new avenues for real-time characterization in experimental condensed matter research.
| Broad physics domain | Condensed Matter Physics / Experimental Spectroscopy of Quantum Materials |
|---|---|
| AI/ML technique(s) to be presented | Deep learning (convolutional neural networks), generative modeling (GANs, denoising diffusion probabilistic models – DDPM), representation learning from noisy data, and traditional machine learning methods (random forest, logistic regression, support vector machines, etc.) |
