ArticleNational science review2023
Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images.
Article in National science review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Atomic-resolution imaging reveals nucleus-free crystallization in two-dimensional amorphous ice on graphite.Nature communications · 2025Article
- Analysis of biofilm assembly by large area automated AFM.NPJ biofilms and microbiomes · 2025Article
- Article
- Article
- Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks.ACS applied materials & interfaces · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Relevant to broad applied fields and natural processes, interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientations of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other scientific studies involving sophisticated experimental results.
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