English
Related papers

Related papers: Atomic Force Microscopy Simulations for CO-functio…

200 papers

Recent advancements in deep learning for predicting 3D protein structures have shown promise, particularly when leveraging inputs like protein sequences and Cryo-Electron microscopy (Cryo-EM) images. However, these techniques often fall…

Computer Vision and Pattern Recognition · Computer Science 2024-08-13 Jaydeep Rade , Ethan Herron , Soumik Sarkar , Anwesha Sarkar , Adarsh Krishnamurthy

Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML…

Chemical Physics · Physics 2025-02-04 Ming Han , Ge Sun , Juan J. de Pablo

A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation software but are computationally expensive. We present a…

High Energy Physics - Experiment · Physics 2026-05-20 Foma Shipilov , Alexander Barnyakov , Artem Ivanov , Fedor Ratnikov

The ongoing development of single electron, nano and atomic scale semiconductor devices would benefit greatly from a characterization tool capable of detecting single electron charging events with high spatial resolution, at low…

Mesoscale and Nanoscale Physics · Physics 2024-03-22 José Bustamante , Yoichi Miyahara , Logan Fairgrieve-Park , Kieran Spruce , Patrick See , Neil Curson , Taylor Stock , Peter Grutter

Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such…

High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN)…

Image and Video Processing · Electrical Eng. & Systems 2021-08-29 Mingfeng Jiang , Minghao Zhi , Liying Wei , Xiaocheng Yang , Jucheng Zhang , Yongming Li , Pin Wang , Jiahao Huang , Guang Yang

Widefield microscopy methods applied to optically thick specimens are faced with reduced contrast due to spatial crosstalk, in which the signal at each point is the result of a superposition from neighboring points that are simultaneously…

With the invention of scanning probe techniques, direct imaging of single atoms and molecules became possible. Today, scanning tunnelling microscopy (STM) routinely provides angstrom-scale image resolution. At the same time, however, STM…

Mesoscale and Nanoscale Physics · Physics 2024-01-30 C. Weiss , C. Wagner , C. Kleimann , F. S. Tautz , R. Temirov

Atomic force microscopy (AFM) is a well-known tool for studying surface roughness and to collect depth information about features on the top atomic layer of samples. By combining secondary ion mass spectroscopy (SIMS) with focused ion beam…

Materials Science · Physics 2022-10-18 Lex Pillatsch , Szilvia Kalácska , Xavier Maeder , Johann Michler

Due to the rapid growth of Electrical Capacitance Tomography (ECT) applications in several industrial fields, there is a crucial need for developing high quality, yet fast, methodologies of image reconstruction from raw capacitance…

Image and Video Processing · Electrical Eng. & Systems 2024-04-09 Wael Deabes , Alaa E. Abdel-Hakim

Knowledge of surface forces is the key to understanding a large number of processes in fields ranging from physics to material science and biology. The most common method to study surfaces is dynamic atomic force microscopy (AFM). Dynamic…

Mesoscale and Nanoscale Physics · Physics 2013-02-06 Daniel Platz , Daniel Forchheimer , Erik A. Tholen , David B. Haviland

We present the design and implementation of a scanning probe microscope, which combines electrically detected magnetic resonance (EDMR) and (photo-)conductive atomic force microscopy ((p)cAFM). The integration of a 3-loop 2-gap X-band…

The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that…

Materials Science · Physics 2023-05-31 Abid Khan , Chia-Hao Lee , Pinshane Y. Huang , Bryan K. Clark

Atomic force microscopy (AFM) is one of the most promising methods for investigating the structure of materials at the micro and nanoscale levels, as well as their local physical-mechanical properties. The experimental data obtained with…

Materials Science · Physics 2018-05-07 Oleg K. Garishin , Roman I. Izyumov , Alexander L. Svistkov

Machine learning methods have nowadays become easy-to-use tools for constructing high-dimensional interatomic potentials with ab initio accuracy. Although machine learned interatomic potentials are generally orders of magnitude faster than…

Computational Physics · Physics 2021-02-24 Yaolong Zhang , Ce Hu , Bin Jiang

Optical nanoscopy is crucial in life and materials sciences, revealing subtle cellular processes and nanomaterial properties. Scattering-type Scanning Near-field Optical Microscopy (s-SNOM) provides nanoscale resolution, relying on the…

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales…

Molecular dynamics simulations have been performed to understand true atomic resolution, which has been observed on the Si(111)-7$\times$7 surface by dynamic force microscopy in ultra high vacuum(UHV). Stable atomic-scale contrast is…

Materials Science · Physics 2009-10-31 Abduxukur Abdurixit , Alexis Baratoff , Ernest Meyer

We demonstrate the application of Atomic Force Microscopy (AFM) based optical force microscopy to map the optical near-fields with nanometer resolution, limited only by the AFM probe geometry. We map the electric field distributions of…

We push the boundaries of electronic structure-based \textit{ab-initio} molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine…