相关论文: A hybrid machine learning model to study UV-Vis sp…

Within first-principles density functional theory (DFT) frameworks, accurate but fast prediction of electronic structures of nanoparticles (NPs) remains challenging. Herein, we propose a machine-learning architecture to rapidly but…

Graph neural networks (GNN) are an effective framework that exploit inter-relationships within graph-structured data for learning. Principal component analysis (PCA) involves the projection of data on the eigenspace of the covariance matrix…

Computational modeling and accurate simulations of localized surface plasmon resonance (LSPR) absorption properties are reported for gold nanobipyramids (GNBs), a class of metal nanoparticle that features highly tunable, geometrydependent…

Anisotropic metal nanostructures exhibit polarization-dependent light scattering. This property has been widely exploited to determine geometries of subwavelength structures using far-field microscopy. Here, we explore the use of…

UV-Vis absorption spectroscopy is frequently used to characterize the size and shape of gold nanoparticles. We present a full-spectrum model that yields reliable results for the commonly encountered case of mixtures of spheres and rods in…

In this work, we use artificial neural networks (ANNs) to recognize the material composition, sizes of nanoparticles and their concentrations in different media with high accuracy, solely from the absorbance spectrum of a macroscopic…

This study presents a comprehensive simulation-based optimization of gallium nitride (GaN)-based metal-semiconductor-metal (MSM) photodetectors designed for ultraviolet (UV) applications. The proposed device architecture incorporates a…

Laser induced breakdown spectroscopy technique is employed for quantitative analysis of aluminum samples by different classical machine learning approaches. A Q-switch Nd:YAG laser at fundamental harmonic of 1064 nm is utilized for creation…

Electron microscopy, while reliable, is an expensive, slow, and inefficient technique for thorough size distribution characterization of both mono- and polydisperse colloidal nanoparticles. If rapid in-situ characterization of colloid…

Principal component analysis (PCA) is a commonly used pattern analysis method that maps high-dimensional data into a lower-dimensional space maximizing the data variance, that results in the promotion of separability of data. Inspired by…

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically…

Large area field electron emitters, typically consisting of several thousands of nanotips, pose a major challenge since numerical modeling requires enormous computational resources. We propose a hybrid approach where the local electrostatic…

We demonstrate wide-field optical microscopy of the pressure distribution at approximately 20 GPa in a diamond anvil cell (DAC), using nitrogen-vacancy (NV) centers in nanodiamonds (NDs) as quantum sensors. Pressure and non-hydrostaticity…

One of the outstanding analytical problems in X-ray single particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and that even…

We apply Principal Component Analysis (PCA) to ~100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong non-linear variation of spectra with effective temperature, the sample is binned into 0.02 mag…

Tensor robust principal component analysis (robust PCA) has been applied to the lightning images. Robust PCA aims to classify the images into low-rank and sparse components. The low rank and sparse components correspond to static background…

A novel hybrid method based on Mie theory and the Discrete Dipole Approximation (DDA) was developed to study the microscopic parameters governing the optical response of tunable photonic crystals (PC). The method is based on a two-step…

In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as…

In this work, optimized size distribution and optical properties in colloidal synthesis of gold nanoparticles (GNPs) were obtained using a proposed ultrasonic Tuerkevich-Frens method. The effect of three ultrasound (20 kHz) irradiation…

Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale…