期刊
ACS NANO
卷 16, 期 1, 页码 89-97出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsnano.1c06840
关键词
atomic force microscopy; machine learning; tip functionalization; chemical identification; electrostatics
类别
资金
- European Research Council (ERC 2017 AdG) [788185]
- Academy of Finland [311012, 314862, 314882, 284621, 318995, 320555]
- World Premier International Research Center Initiative (WPI), MEXT, Japan
- European Union [845060, 897828]
- Academy of Finland (AKA) [314882, 314882, 311012, 311012] Funding Source: Academy of Finland (AKA)
- Marie Curie Actions (MSCA) [845060, 897828] Funding Source: Marie Curie Actions (MSCA)
In this study, a machine learning based atomic force microscopy method is proposed to provide immediate maps of electrostatic potential directly from images, offering reliable atomic scale electrostatic characterization with minimal computational overhead.
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.
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