Electrostatic Discovery Atomic Force Microscopy

被引:14
|
作者
Oinonen, Niko [1 ]
Xu, Chen [1 ]
Alldritt, Benjamin [1 ]
Canova, Filippo Federici [1 ,2 ]
Urtev, Fedor [1 ,3 ]
Cai, Shuning [1 ]
Krejci, Ondrej [1 ]
Kannala, Juho [3 ]
Liljeroth, Peter [1 ]
Foster, Adam S. [1 ,4 ]
机构
[1] Aalto Univ, Dept Appl Phys, Helsinki 00076, Finland
[2] Nanolayers Res Comp Ltd, London N12 0HL, England
[3] Aalto Univ, Dept Comp Sci, Helsinki 00076, Finland
[4] Kanazawa Univ, WPI Nano Life Sci Inst WPI NanoLSI, Kakuma Machi, Kanazawa, Ishikawa 9201192, Japan
基金
芬兰科学院; 欧洲研究理事会;
关键词
atomic force microscopy; machine learning; tip functionalization; chemical identification; electrostatics; SCANNING PROBE MICROSCOPY; SMART DATA; MOLECULE; DEFECTS; DEEP; BIG;
D O I
10.1021/acsnano.1c06840
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
While offering high resolution atomic and electronic structure, scanning probe microscopy techniques have found greater challenges in providing reliable electrostatic characterization on the same scale. In this work, we offer electrostatic discovery atomic force microscopy, a machine learning based method which provides immediate maps of the electrostatic potential directly from atomic force microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach offers reliable atomic scale electrostatic maps on any system with minimal computational overhead.
引用
收藏
页码:89 / 97
页数:9
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