Recent advances in theoretical development of thermal atomic layer deposition: a review

M Shahmohammadi, R Mukherjee, C Sukotjo… - Nanomaterials, 2022 - mdpi.com
Atomic layer deposition (ALD) is a vapor-phase deposition technique that has attracted
increasing attention from both experimentalists and theoreticians in the last few decades …

Machine learning (ML)‐assisted design and fabrication for solar cells

F Li, X Peng, Z Wang, Y Zhou, Y Wu… - Energy & …, 2019 - Wiley Online Library
Photovoltaic (PV) technologies have attracted great interest due to their capability of
generating electricity directly from sunlight. Machine learning (ML) is a technique for …

A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment

X Wang, Y Wang, L Zhang, F Dai, H Wang - Nuclear Fusion, 2022 - iopscience.iop.org
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics
(MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the …

Artificial neural network discrimination for parameter estimation and optimal product design of thin films manufactured by chemical vapor deposition

G Kimaev, LA Ricardez-Sandoval - The Journal of Physical …, 2020 - ACS Publications
Industrial production of valuable chemical products often involves the manipulation of
phenomena evolving at different temporal and spatial scales. Product properties can be …

Machine learning for reparameterization of four-site water models: TIP4P-BG and TIP4P-BGT

H Ye, J Wang, Y Zheng, H Zhang… - Physical Chemistry …, 2021 - pubs.rsc.org
Parameterizing an effective water model is a challenging issue because of the difficulty in
maintaining a comprehensive balance among the diverse physical properties of water with a …

Atomistic simulation of physical vapor deposition of optical thin films

FV Grigoriev, VB Sulimov - Nanomaterials, 2023 - mdpi.com
A review of the methods and results of atomistic modeling of the deposition of thin optical
films and a calculation of their characteristics is presented. The simulation of various …

Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells

N Tomar, G Rani, VS Dhaka… - International Journal of …, 2022 - Wiley Online Library
Photovoltaic technology attracts researchers from industry and academia due to its potential
in producing electricity directly from the sunlight. Among all the photovoltaic devices, the dye …

Deep neural network potential for simulating hydrogen blistering in tungsten

XY Wang, YN Wang, K Xu, FZ Dai, HF Liu, GH Lu… - Physical Review …, 2023 - APS
Tungsten is a promising candidate for the plasma-facing material in fusion energy facilities,
however, the low-energy, high-flux hydrogen plasma causes severe blistering in tungsten …

Deep learning-based neural network potential for investigating the synergistic effect of H and He in BCC-Fe

F Wu, Z Liu, Y Chen, X Guo, J Xue, Y Li… - Computational Materials …, 2025 - Elsevier
Reduced activated ferrite/martensite (RAFM) steel is a prospective structural material for
fusion reactors. The interaction of deuterium/tritium fusion neutrons with structural materials …

Application of a large-scale molecular dynamics approach to modelling the deposition of TiO2 thin films

FV Grigoriev, VB Sulimov, AV Tikhonravov - Computational Materials …, 2021 - Elsevier
The previously developed large-scale molecular dynamics approach is applied to high-
performance parallel modelling the deposition of TiO 2 thin films. The largest simulated …