用多模态视觉分析材料微观结构中的有序演化。
Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision
- 融合电子显微与光谱数据的机器学习方法
- 多模态模型性能显著优于单一模态
- 适用于材料结构机理研究与高性能材料设计
微电子、储能及极端环境用高性能材料的发展依赖于对决定性能的微观结构有序性的描述与调控。当前认知主要来自耗时的手动分析成像与光谱数据，难以扩展、复现困难，且无法揭示构建机理模型所需的潜在关联。本文展示了一种基于多模态机器学习的分析方法，用于解析复杂氧化物La$_{1-x}$Sr$_x$FeO$_3$的电子显微图像中的结构有序性。我们构建了结合全监督与半监督分类的混合流程，可评估各数据模态的特征及其对集成模型的贡献。结果表明，单模态与多模态模型表现存在明显差异，为利用计算机视觉描述晶体有序性提供了通用启示。
原文摘要 · Abstract (English)
The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。