Seeing the Invisible: Machine learning-Based QPI Kernel Extraction via Latent Alignment
This addresses a domain-specific problem for researchers in quantum materials, offering an incremental improvement in QPI analysis.
The paper tackled the ill-posed inverse problem of extracting single-scatterer QPI kernels from multi-scatterer images in quantum materials, proposing an AI-based framework that achieved significantly higher extraction accuracy and improved generalization to unseen kernels compared to a baseline.
Quasiparticle interference (QPI) imaging is a powerful tool for probing electronic structures in quantum materials, but extracting the single-scatterer QPI pattern (i.e., the kernel) from a multi-scatterer image remains a fundamentally ill-posed inverse problem. In this work, we propose the first AI-based framework for QPI kernel extraction. We introduce a two-step learning strategy that decouples kernel representation learning from observation-to-kernel inference. In the first step, we train a variational autoencoder to learn a compact latent space of scattering kernels. In the second step, we align the latent representation of QPI observations with those of the pre-learned kernels using a dedicated encoder. This design enables the model to infer kernels robustly even under complex, entangled scattering conditions. We construct a diverse and physically realistic QPI dataset comprising 100 unique kernels and evaluate our method against a direct one-step baseline. Experimental results demonstrate that our approach achieves significantly higher extraction accuracy, and improved generalization to unseen kernels.