相关论文: Tracking atomic structure evolution during directe…
The design and preparation of novel quantum materials with atomic precision are crucial for exploring new physics and for device applications. Electron irradiation has demonstrated as an effective method for preparing novel quantum…
We explore the emergence and active control of optical bistability in a two-level atom near a graphene sheet. Our theory incorporates self-interaction of the optically-driven atom and its coupling to electromagnetic vacuum modes, both of…
We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We…
We have studied the effect of photoelectrons on defect formation in graphene during extreme ultraviolet (EUV) irradiation. Assuming the major role of these low energy electrons, we have mimicked the process by using low energy primary…
In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime.…
Existing defects in software components is unavoidable and leads to not only a waste of time and money but also many serious consequences. To build predictive models, previous studies focus on manually extracting features or using tree…
Strong gravitational lensing provides valuable insights into the mass distribution of galaxies and the nature of dark matter. However, its modeling is computationally demanding due to the large volume of strong lensing observations. In this…
The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an…
Freestanding graphene displays an outstanding resilience to electron irradiation at low electron energies. Point defects in graphene are, however, subject to beam driven dynamics. This means that high resolution micrographs of point…
It has long been an ultimate goal to introduce chemical doping at the atomic level to precisely tune properties of materials. Two-dimensional materials have natural advantage because of its highly-exposed surface atoms, however, it is still…
The doping of graphene to tune its electronic structure is essential for its further use in carbon based electronics. Adapting strategies from classical silicon based semiconductor technology, we use the incorporation of heteroatoms in the…
The prosperity of computer vision (CV) and natural language procession (NLP) in recent years has spurred the development of deep learning in many other domains. The advancement in machine learning provides us with an alternative option…
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the performance of a graph neural network in…
Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep neural network that uses not only features focusing on atoms…
Towards spin selective electronics made of three coordinated carbon atoms, here we computationally propose robust and reversibly bias driven evolution of pristine undoped graphene nano-ribbons(GNR) into ferromagnetic-semiconductor, metal or…
The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage detection in…
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however,…
The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour…
We explore the possibility for the reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. Here, scanning transmission electron microscopy (STEM)…
The recent progress in high-resolution transmission electron microscopy (HRTEM) has given rise to the possibility of in situ observations of nanostructure transformations and chemical reactions induced by electron irradiation. In this…