Machine learning for perovskite materials design and discovery
Q Tao, P Xu, M Li, W Lu - Npj computational materials, 2021 - nature.com
The development of materials is one of the driving forces to accelerate modern scientific
progress and technological innovation. Machine learning (ML) technology is rapidly …
progress and technological innovation. Machine learning (ML) technology is rapidly …
Machine learning in perovskite solar cells: recent developments and future perspectives
Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
An extensive theoretical quantification of secondary electron emission from silicon
Though intensive experimental studies have been carried out on electron emission
properties in past decades, the reliable data from accurate experimental measurements for …
properties in past decades, the reliable data from accurate experimental measurements for …
[HTML][HTML] Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation
We present an up-to-date Monte Carlo simulation of electron backscattering coefficient of
beryllium, which is an important material in fusion reactor, at an impact energy range of …
beryllium, which is an important material in fusion reactor, at an impact energy range of …
Uncertainty evaluation of Monte Carlo simulated line scan profiles of a critical dimension scanning electron microscope (CD-SEM)
In recent years, precision and accuracy for a more precise critical dimension (CD) control
have been required in CD measurement technology. CD distortion between the …
have been required in CD measurement technology. CD distortion between the …
Charging effect induced by electron beam irradiation: A review
Charging effect frequently occurs when characterizing nonconductive materials using
electrons as probes and/or signals and can impede the acquisition of useful information …
electrons as probes and/or signals and can impede the acquisition of useful information …
Electron backscattering coefficients of molybdenum and tungsten based on the Monte Carlo simulations
Monte Carlo simulation is employed for the calculation of electron backscattering coefficients
of molybdenum (Mo) and tungsten (W) at normal incidence angle and at energies between …
of molybdenum (Mo) and tungsten (W) at normal incidence angle and at energies between …
Crystal structural prediction of perovskite materials using machine learning: A comparative study
Abstract In this study, Machine Learning (ML) techniques have been exploited to classify the
crystal structure of ABO 3 perovskite compounds. In the present work, seven different ML …
crystal structure of ABO 3 perovskite compounds. In the present work, seven different ML …
Emission of the backscattered electron in the energy range of 20 to100 keV
A Xie, Y Liu, HJ Dong - Annals of Nuclear Energy, 2024 - Elsevier
This paper presented the theoretical model of backscattered electron emission BEE, and the
universal formulas for some parameters such as η (h, E po, Z) and f (x, E po, Z) at E po= 20 …
universal formulas for some parameters such as η (h, E po, Z) and f (x, E po, Z) at E po= 20 …
A comprehensive open‐access database of electron backscattering coefficients for energies ranging from 0.1 keV to 15 MeV
F Akbari - Medical Physics, 2023 - Wiley Online Library
Purpose The characterization of electron backscattering is essential in medical physics for
accurately assessing dose deposited around inhomogeneities where backscattering alters …
accurately assessing dose deposited around inhomogeneities where backscattering alters …