A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns

2026-09-18

AI Summary

本文构建了4D-ImageNet数据集，包含174,000个衍射图案，用于解决4D-STEM产生的大数据量处理问题，并提供基准方法以支持多种分析任务。

Abstract

Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yielding spatially resolved reciprocal-space information but large, heterogeneous data volumes. Here we describe 4D-ImageNet, a collection of 174,000 diffraction patterns comprising 145,000 experimental patterns selected from 29 acquisitions and 29,000 multislice simulations. The experimental data cover acquisition-level labels for Ag, Au, mixed Au-Ag, CoO, Pd and ZnO specimens across multiple fields of view, scan dimensions, camera lengths and exposure times. Each acquisition contributes 5,000 quality-ranked patterns with source scan coordinates and acquisition metadata. A set-prediction detector provides model-derived pseudo-labels for the direct-beam position and Bragg-disk centres, with a confidence score for each disk. The simulation data cover 13 crystal structures and include Euler rotations, reciprocal-space sampling and approximate low-index beam directions. A grouped mixed-domain masked-reconstruction benchmark is provided to assess leakage-resistant loading and evaluation across experimental and simulated data. The dataset is intended for representation learning, disk detection, diffraction-pattern retrieval, orientation analysis and simulation-to-experiment studies.

Problem

• 4D-STEM
• diffraction patterns
• dataset
• benchmark
• heterogeneous data

Innovation

• 4D-ImageNet
• set-prediction detector
• mixed-domain masked-reconstruction benchmark
• diffraction patterns
• representation learning