Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images.

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

SUBMITTER: Tang B 

PROVIDER: S-EPMC10232042 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature