Multi4D: an end-to-end neural network for structural determination at complex material interfaces

2026-09-13

AI Summary

为解决复杂材料界面结构难以解析的问题，提出Multi4D神经网络框架，利用4D-STEM数据生成高精度晶体学图谱。

Abstract

Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations with macroscopic failure mechanisms to guide future materials design. Yet structural heterogeneity, phase overlap, and local disorder produce highly convoluted diffraction signatures, making extended transition regions difficult to interpret at atomic resolution across large fields of view. Here, we introduce Multi4D, a physics-informed neural network framework for automated multi-component crystallographic identification using four-dimensional scanning transmission electron microscopy (4D-STEM). By combining a latent-space Diffusion Transformer for physics-constrained style translation with a rotation-invariant convolutional neural network for orientation-agnostic classification, this approach translates multi-components diffraction datasets into deterministic crystallographic maps with 98.82% accuracy. In addition, we introduce Diffraction-Inferred Structural Complexity as an information-theoretic entropy metric derived from classifier predictive uncertainty that quantifies local structural ambiguity. We apply Multi4D to generate high-fidelity structural maps of complex superconducting heterostructures, corroded alloy surfaces, and degraded solid-state battery interfaces down to single-nanometer spatial resolution. This framework establishes a statistically robust analytical paradigm for automated microscopy, facilitating both industrial quality control and the data-driven discovery of interfacial design principles.

Problem

• structural determination
• complex material interfaces
• diffraction signatures
• heterogeneous interfaces
• local structural variations

Innovation

• Multi4D
• Physics-informed Neural Network
• 4D-STEM
• Diffusion Transformer
• Structural Complexity