Physics aware AI improves identification of 2D quantum materials
QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
Identifying ultra-thin 2D quantum materials using microscope images is hard because these materials look different depending on the lab and setup used. The authors created a computer system called QuPAINT that uses physics knowledge and synthetic training images to better find and understand these tiny materials in real microscope pictures. They also made a large test dataset to check the system's accuracy. QuPAINT works better than earlier methods, especially at detecting single-layer flakes, and stays reliable even when shown new types of materials.
What this means in practice
- •For quantum device engineers: Accurately select and identify thin quantum material flakes from microscopy images for building nanoscale devices.
- •For microscopy imaging teams: Improve automated interpretation of complex microscope images across varied labs and substrates using physics-aware AI.
- •For quality control teams: Enhance inspection accuracy of microscopic layered materials in manufacturing through reliable layer counting and identification.$Commercial implications: Enables selling advanced inspection tools that precisely detect material layers for electronics manufacturers ensuring product reliability.
Authors
Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu
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.