Study on growth rate of TiO2
 nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network

Alireza Bahramian

doi:10.1002/sia.5314

Study on growth rate of TiO2 nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network

Surface and Interface Analysis ◽

10.1002/sia.5314 ◽

2013 ◽

Vol 45 (11-12) ◽

pp. 1727-1736 ◽

Cited By ~ 12

Author(s):

Alireza Bahramian

Keyword(s):

Neural Network ◽

Thin Films ◽

Molecular Dynamics ◽

Growth Rate ◽

Artificial Neural Network ◽

Nanostructured Thin Films ◽

Artificial Neural

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Artificial neural network correction for density-functional tight-binding molecular dynamics simulations

MRS Communications ◽

10.1557/mrc.2019.80 ◽

2019 ◽

Vol 9 (3) ◽

pp. 867-873 ◽

Cited By ~ 6

Author(s):

Junmian Zhu ◽

Van Quan Vuong ◽

Bobby G. Sumpter ◽

Stephan Irle

Keyword(s):

Neural Network ◽

Molecular Dynamics ◽

Artificial Neural Network ◽

Molecular Dynamics Simulations ◽

Density Functional ◽

Tight Binding ◽

Artificial Neural ◽

Dynamics Simulations ◽

Image Position

Abstract

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Artificial Neural Network Discrimination for Parameter Estimation and Optimal Product Design of Thin Films Manufactured by Chemical Vapor Deposition

The Journal of Physical Chemistry C ◽

10.1021/acs.jpcc.0c05250 ◽

2020 ◽

Vol 124 (34) ◽

pp. 18615-18627

Author(s):

Grigoriy Kimaev ◽

Luis A. Ricardez-Sandoval

Keyword(s):

Neural Network ◽

Thin Films ◽

Artificial Neural Network ◽

Parameter Estimation ◽

Chemical Vapor Deposition ◽

Product Design ◽

Vapor Deposition ◽

Chemical Vapor ◽

Optimal Product Design ◽

Artificial Neural

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Comparison between artificial neural network and linear model prediction performance for FDI disparity and the growth rate of companies in Hungarian counties.

International Journal of Business Information Systems ◽

10.1504/ijbis.2020.10034502 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Devesh Singh

Keyword(s):

Neural Network ◽

Growth Rate ◽

Artificial Neural Network ◽

Linear Model ◽

Model Prediction ◽

Prediction Performance ◽

Artificial Neural

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Nutrients interaction investigation to improve Monascus purpureus FTC5391 growth rate using Response Surface Methodology and Artificial Neural Network

Malaysian Journal of Microbiology ◽

10.21161/mjm.44912 ◽

2013 ◽

Author(s):

Ajdari, Z. ◽

Ebrahimpour, A. ◽

Abdul Manan, M. ◽

Ajdari, D. ◽

Abbasiliasi, S. ◽

...

Keyword(s):

Neural Network ◽

Growth Rate ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Monascus Purpureus ◽

Artificial Neural

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Analysis of crystallization phenomenon in Indian honey using molecular dynamics simulations and artificial neural network

Food Chemistry ◽

10.1016/j.foodchem.2019.125182 ◽

2019 ◽

Vol 300 ◽

pp. 125182 ◽

Cited By ~ 3

Author(s):

Rishi Ravindra Naik ◽

Neha S. Gandhi ◽

Mamta Thakur ◽

Vikas Nanda

Keyword(s):

Neural Network ◽

Molecular Dynamics ◽

Artificial Neural Network ◽

Molecular Dynamics Simulations ◽

Artificial Neural ◽

Dynamics Simulations

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Theory of artificial-neural-network-based simultaneous optical sensing of two analytes using sculptured thin films

Journal of Nanophotonics ◽

10.1117/1.jnp.15.036012 ◽

2021 ◽

Vol 15 (03) ◽

Author(s):

Patrick D. McAtee ◽

Akhlesh Lakhtakia

Keyword(s):

Neural Network ◽

Thin Films ◽

Artificial Neural Network ◽

Optical Sensing ◽

Sculptured Thin Films ◽

Artificial Neural

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Determination of the composition of light thin films with artificial neural network analysis of Rutherford backscattering experiments

Physical Review E ◽

10.1103/physreve.67.046705 ◽

2003 ◽

Vol 67 (4) ◽

Cited By ~ 4

Author(s):

V. Matias ◽

G. Öhl ◽

J. C. Soares ◽

N. P. Barradas ◽

A. Vieira ◽

...

Keyword(s):

Neural Network ◽

Thin Films ◽

Artificial Neural Network ◽

Network Analysis ◽

Rutherford Backscattering ◽

Neural Network Analysis ◽

Artificial Neural Network Analysis ◽

Artificial Neural

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Efficient prediction of new product growth rate: A comparison between Artificial Neural Network and conventional method

2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) ◽

10.1109/iceeot.2016.7755503 ◽

2016 ◽

Author(s):

Vikas Bhatnagar ◽

Ritanjali Majhi ◽

S.L. Tulasi Devi

Keyword(s):

Neural Network ◽

Growth Rate ◽

Artificial Neural Network ◽

Conventional Method ◽

New Product ◽

Efficient Prediction ◽

Artificial Neural ◽

Product Growth Rate

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Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se

The Journal of Chemical Physics ◽

10.1063/1.5116420 ◽

2019 ◽

Vol 151 (12) ◽

pp. 124303 ◽

Cited By ~ 2

Author(s):

Kohei Shimamura ◽

Shogo Fukushima ◽

Akihide Koura ◽

Fuyuki Shimojo ◽

Masaaki Misawa ◽

...

Keyword(s):

Neural Network ◽

Molecular Dynamics ◽

Artificial Neural Network ◽

First Principles ◽

Interatomic Potential ◽

Artificial Neural ◽

First Principles Molecular Dynamics

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Analysis of phthalate plasticizer migration from PVDC packaging materials to food simulants using molecular dynamics simulations and artificial neural network

Food Chemistry ◽

10.1016/j.foodchem.2020.126465 ◽

2020 ◽

Vol 317 ◽

pp. 126465 ◽

Cited By ~ 5

Author(s):

Xiujuan Wang ◽

Meng Song ◽

Suting Liu ◽

Sizhu Wu ◽

Aung Myat Thu

Keyword(s):

Neural Network ◽

Molecular Dynamics ◽

Artificial Neural Network ◽

Molecular Dynamics Simulations ◽

Packaging Materials ◽

Food Simulants ◽

Phthalate Plasticizer ◽

Plasticizer Migration ◽

Artificial Neural ◽

Dynamics Simulations

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