.png){kind=icon}
返回
Molecule graph reconstruction from atomic force microscope images with machine learning
.png){kind=icon}
OA
AI
DOI：10.1557/s43577-022-00324-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Despite the success of noncontact atomic force microscopy (AFM) in providing atomic-scale insight into the structure and properties of matter on surfaces, the wider applicability of the technique faces challenges in the difficulty of interpreting the measurement data. We tackle this problem by proposing a machine learning model for extracting molecule graphs of samples from AFM images. The predicted graphs contain not only atoms and their bond connections but also their coordinates within the image and elemental identification. The model is shown to be effective on simulated AFM images, but we also highlight some issues with robustness that need to be addressed before generalization to real AFM images. Impact statement Developing better techniques for imaging matter at the atomic scale is important for advancing our fundamental understanding of physics and chemistry as well as providing better tools for materials R&D of nanotechnologies. State-of-the-art high-resolution atomic force microscopy experiments are providing such atomic-resolution imaging for many systems of interest. However, greater automation of processing the measurement data is required in order to eliminate the need for subjective evaluation by human operators, which is unreliable and requires specialized expertise. The ability to convert microscope images into graphs would provide an easily understandable and precise view into the structure of the system under study. Furthermore, a graph consisting of a discrete set of objects, rather than an image that describes a continuous domain, is much more amenable to further processing and analysis using symbolic reasoning based on physically motivated rules. This type of image-to-graph conversion is also relevant to other machine learning tasks such as scene understanding.
Keyword:
Scanning probe microscopy
Atomic force microscopy
Machine learning
Graph neural network
期刊
IF:
4.9
论文数:
5.3K
被引数:
9.5K
机构
引用论文
Molecular Identification, Bond Order Discrimination, and Apparent Intermolecular Features in Atomic Force Microscopy Studied with a Charge Density Based Method使用基于电荷密度的方法研究原子力显微镜中的分子识别，键序区分和表观分子间特征.png)
ACS NANO
ACS NANOIF16
PREAI
分享收藏Mechanism of high-resolution STM/AFM imaging with functionalized tips功能化尖端的高分辨率STM/AFM成像机理PHYSICAL REVIEW B
OAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm基准材料性能预测方法: Matbench测试集和Automatminer参考算法OAAI
OAAI分享收藏
查看更多内容