Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

Gordon, Oliver; Moriarty, Philip

doi:10.1088/2632-2153/ab7d2f

Cited by 33 publications

(31 citation statements)

References 78 publications

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“…Our methodological approach builds upon the general philosophy of using machine learning to handle data analysis challenges in Scanning Probe Microscopy (SPM) [46][47][48][49] and the specic use of deep learning Convolutional Neural Networks (CNN) 50 to recognize features in high-resolution SPM images. Recent examples include conditioning of SPM tips, 51 identication of defects with STM 52,53 and nanostructures with AFM, 54 and making molecular structure predictions from AFM images.…”

mentioning

confidence: 99%

Predicting hydration layers on surfaces using deep learning

2021

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confidence: 99%

Predicting hydration layers on surfaces using deep learning

2021

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“…Breaking of cantilever tip after long period of functionalization and damaging live cell samples due to lack of optimization of the loading forces are major problems that make this method low throughput (Xie and Ren 2019a;Xie and Ren 2019b). Over the past couple of years, researchers in the material science community have begun to combine artificial intelligence (AI) and machine learning approaches (Huang et al, 2018;Müller et al, 2019;Alldritt et al, 2020;Gordon and Moriarty 2020;Krull et al, 2020) with AFM for various pattern recognition and data post-processing tasks. Also, some initial research happened to select appropriate AFM scanning areas and data modeling using deep learning.…”

Section: Discussionmentioning

confidence: 99%

Biosensing, Characterization of Biosensors, and Improved Drug Delivery Approaches Using Atomic Force Microscopy: A Review

Sarkar

2022

Front. Nanotechnol.

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Since its invention, atomic force microscopy (AFM) has come forth as a powerful member of the “scanning probe microscopy” (SPM) family and an unparallel platform for high-resolution imaging and characterization for inorganic and organic samples, especially biomolecules, biosensors, proteins, DNA, and live cells. AFM characterizes any sample by measuring interaction force between the AFM cantilever tip (the probe) and the sample surface, and it is advantageous over other SPM and electron micron microscopy techniques as it can visualize and characterize samples in liquid, ambient air, and vacuum. Therefore, it permits visualization of three-dimensional surface profiles of biological specimens in the near-physiological environment without sacrificing their native structures and functions and without using laborious sample preparation protocols such as freeze-drying, staining, metal coating, staining, or labeling. Biosensors are devices comprising a biological or biologically extracted material (assimilated in a physicochemical transducer) that are utilized to yield electronic signal proportional to the specific analyte concentration. These devices utilize particular biochemical reactions moderated by isolated tissues, enzymes, organelles, and immune system for detecting chemical compounds via thermal, optical, or electrical signals. Other than performing high-resolution imaging and nanomechanical characterization (e.g., determining Young’s modulus, adhesion, and deformation) of biosensors, AFM cantilever (with a ligand functionalized tip) can be transformed into a biosensor (microcantilever-based biosensors) to probe interactions with a particular receptors of choice on live cells at a single-molecule level (using AFM-based single-molecule force spectroscopy techniques) and determine interaction forces and binding kinetics of ligand receptor interactions. Targeted drug delivery systems or vehicles composed of nanoparticles are crucial in novel therapeutics. These systems leverage the idea of targeted delivery of the drug to the desired locations to reduce side effects. AFM is becoming an extremely useful tool in figuring out the topographical and nanomechanical properties of these nanoparticles and other drug delivery carriers. AFM also helps determine binding probabilities and interaction forces of these drug delivery carriers with the targeted receptors and choose the better agent for drug delivery vehicle by introducing competitive binding. In this review, we summarize contributions made by us and other researchers so far that showcase AFM as biosensors, to characterize other sensors, to improve drug delivery approaches, and to discuss future possibilities.

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“…The method can also be applied to images of systems where direct simulation is impossible due to size, complexity or lack of information. Combined with developments in the autonomous functionalization of the tip in SPM [36], this promises a future of potential in electrostatics for ED-AFM.…”

Section: Discussionmentioning

confidence: 99%

“…In general, the adoption of ML methods into materials analysis has seen rapid recent growth [28][29][30] and this has been followed by an equivalent growth in its applications to image analysis in SPM [31][32][33][34][35][36]. Here we build upon our ML method for predicting molecular structure from AFM images [37], to predict the electrostatic field of the sample molecule.…”

Section: Methodsmentioning

confidence: 99%

Electrostatic Discovery Atomic Force Microscopy

Oinonen,

Xu,

Alldritt

et al. 2021

Preprint

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While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning based method which provides immediate quantitative 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 opens the door to reliable atomic scale electrostatic maps on any system with minimal computational overhead.

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Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

Cited by 33 publications

References 78 publications

Predicting hydration layers on surfaces using deep learning

Predicting hydration layers on surfaces using deep learning

Biosensing, Characterization of Biosensors, and Improved Drug Delivery Approaches Using Atomic Force Microscopy: A Review

Electrostatic Discovery Atomic Force Microscopy

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