Automated Structure Discovery in Atomic Force Microscopy

Abstract

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules, due to difficulties with interpretation of highly distorted AFM images originating from non-planar molecules. Here we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to apply high-resolution AFM to a large variety of systems for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.

References in corpus (7)

• On representing chemical environments
• Advances in atomic force microscopy
• The mechanism of high-resolution STM/AFM imaging with functionalized tips
• The origin of high-resolution IETS-STM images of organic molecules with functionalized tips
• Intermolecular contrast in atomic force microscopy images without intermolecular bonds
• Investigating Atomic Contrast in Atomic Force Microscopy and Kelvin Probe Force Microscopy on Ionic Systems using Functionalized Tips
• Interatomic force laws that corrupt their own measurement

Cited by in corpus (13)

• Engineering interfacial quantum states and electronic landscapes by molecular nanoarchitectures
• On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
• Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization
• Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks
• Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images
• Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations
• Structure discovery in Atomic Force Microscopy imaging of ice
• Direct imaging of carbohydrate stereochemistry
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
• Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
• Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling
• Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy
• Electrostatic Discovery Atomic Force Microscopy