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.CO-AFM now offers an unprecedented window into molecular structure on surfaces -aside from the detailed resolution of the results of molecular assembly 11,12 , it is possible to study bond order 13 , charge distributions 14,15 and the individual steps of on-surface chemical reactions 16,17,18,19 .As yet, most CO-AFM studies have been focused on planar molecular systems, where the experimental image requires almost no interpretation 10,5,20 . Even where understanding is not immediately obvious, such as due to controversies over the nature of observed bonds 21 , efficient models have been developed 22,12,23,24,25 that explain the contrast mechanism in terms of the tip-surface interaction and CO lateral flexibility. However, the further the systems studied are from two-dimensional molecules containing only hydrogen and carbon, the more complex and time consuming (if not impossible) the interpretation process becomes 17,26,27,28,29 . While recent measurements using rigid O-terminated copper tips makes interpreting images of flat systems even easier 30,31 , the rigidity also means even less atoms can be characterized when moving to 3D systems. In recent years, CO-AFM has moved towards measuring truly unknown structures 29,32,33,34 , where it has overcome many of the limitations of techniques such as nuclear magnetic resonance and mass spectrometry. It is clear that this trend is going to continue, and potentially even accelerate, in particular for innovative studies, e.g. in life sciences or biochemistry 6,7 , demonstrated manifestly in the first CO-AFM images of DNA 35 . Reliable interpretation of such data becomes a vast exploration through all possible molecules, configurations and imaging parameters to find agreement. This is impractical in anything beyond very simple systems, severely limiting the ultimate power of the technique.In this work, we couple a systematic software approach with detailed experimental CO-AFM imaging to understand and predict AFM images for molecules of any size, configuration or orientation without prior knowledge of the system being studied. We use the late...
Despite the success of noncontact atomic force microscopy (AFM) in providing atomic-scale insight into the structure and properties of matter on surfaces, the wider applicability of the technique faces challenges in the difficulty of interpreting the measurement data. We tackle this problem by proposing a machine learning model for extracting molecule graphs of samples from AFM images. The predicted graphs contain not only atoms and their bond connections but also their coordinates within the image and elemental identification. The model is shown to be effective on simulated AFM images, but we also highlight some issues with robustness that need to be addressed before generalization to real AFM images. Impact statement Developing better techniques for imaging matter at the atomic scale is important for advancing our fundamental understanding of physics and chemistry as well as providing better tools for materials R&D of nanotechnologies. State-of-the-art high-resolution atomic force microscopy experiments are providing such atomic-resolution imaging for many systems of interest. However, greater automation of processing the measurement data is required in order to eliminate the need for subjective evaluation by human operators, which is unreliable and requires specialized expertise. The ability to convert microscope images into graphs would provide an easily understandable and precise view into the structure of the system under study. Furthermore, a graph consisting of a discrete set of objects, rather than an image that describes a continuous domain, is much more amenable to further processing and analysis using symbolic reasoning based on physically motivated rules. This type of image-to-graph conversion is also relevant to other machine learning tasks such as scene understanding. Graphical abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.