[Submitted on 10 Sep 2026]
Title:QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization
View a PDF of the paper titled QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization, by Sankalp Pandey and 6 other authors
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Abstract:Characterizing two-dimensional (2D) quantum materials by optical microscopy requires localizing exfoliated flakes and determining their layer thickness from subtle optical contrast and interference color to select suitable flakes for device fabrication. However, models face synthetic-to-real domain shifts and variation across materials, substrates, laboratories, and imaging conditions. We present QuPAINT, a physics-aware multimodal framework for transferable quantum flake characterization. The Synthetic Materials Framework (Synthia) generates diverse synthetic microscopy images while preserving layer-dependent optical behavior. Using these images, we construct QMat-Instruct, a multimodal instruction dataset with image-specific reasoning traces generated from verified annotations and constrained to observable optical cues. QuPAINT integrates these signals through Physics-Informed Attention (PIA), which injects substrate-relative optical priors into the visual representation to support grounded multimodal reasoning. For evaluation, we introduce QF-Bench, to our knowledge, the largest real-world benchmark for this problem, spanning diverse microscopy and substrate conditions. Using its verified annotations, we study counting, visual grounding, reasoning quality, confidence calibration, and transfer to an unseen material. QuPAINT-8B substantially outperforms prior methods and establishes state-of-the-art performance for both general and monolayer flake detection. Additional experiments show that image-grounded supervision improves strict spatial grounding and confidence calibration while preserving robust general flake detection on the unseen material.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2609.12202 [cs.CV]
(or arXiv:2609.12202v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.12202
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sankalp Pandey [view email] [v1] Thu, 10 Sep 2026 20:56:07 UTC (7,057 KB)
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