Deep learning object detection in materials science: Current state and future directions
Deep learning object detection in materials science: Current state and future directions
复制标题
DOI：
10.1016/j.commatsci.2022.111527
复制
发表时间：
2022-05-24
影响因子：
3.3
通讯作者：
Jacobs, Ryan
中科院分区：
文献类型：
--
作者：
Jacobs, Ryan
基于深度学习的物体检测模型最近在材料科学中得到了广泛的应用，在过去两年中取得了快速进展。扫描和隧道电子显微镜方法是最重要和最广泛使用的表征技术之一，用于了解从微米到原子尺度的基本材料结构-性质-性能联系。从现代电子显微镜仪器的数据集的大小和复杂性的显着增加，有必要开发和使用的自动化方法提取相关的图像特征。在这里，使用对象检测材料科学，重点是在电子显微镜图像的功能分析，进行审查。最近的开创性研究的主要发现和局限性，使用对象检测来表征和量化辐照金属合金中的缺陷，分段和分析微米和纳米粒子，在纳米级找到单个原子，并从原位视频中检测和跟踪对象。材料界目前面临的机遇和挑战突出显示，模型评估和适用性的最佳实践的讨论，沿着改进的模型训练与合成数据的潜力。本文最后对更广泛的材料社区构建一个活的生态系统的潜力提供了更多的推测性，前瞻性的想法，该生态系统将社区共识策展数据和验证模型作为工具，以最好地告知对象检测和分割模型在特定材料领域的应用。
Deep learning-based object detection models have recently found widespread use in materials science, with rapid progress made in just the past two years. Scanning and tunneling electron microscopy methods are among the most important and widely used characterization techniques for understanding fundamental materials structure-property-performance linkages from the micron to atomic scale. Dramatic increases in dataset size and complexity from modern electron microscopy instruments have necessitated the development and use of automated methods of extracting pertinent features of images. Here, the use of object detection in materials science, with a focus on the analysis of features in electron microscopy images, is reviewed. Key findings and limitations of recent seminal studies using object detection to characterize and quantify defects in irradiated metal alloys, segment and analyze micro and nanoparticles, find individual atoms at the nanoscale, and detect and track objects from in situ video are reviewed. Opportunities and challenges presently facing the materials community are highlighted, where discussion of best practices for model assessment and applicability are presented, along with the potential of improved model training with synthetic data. This review concludes with offering more speculative, forward-looking thoughts on the potential of the broader materials community to construct a living ecosystem integrating community-consensus curated data and validated models as tools to best inform application of object detection and segmentation models to specific materials domains.