Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes

The Journal of Physical Chemistry B ◽

10.1021/acs.jpcb.6b00787 ◽

2016 ◽

Vol 120 (19) ◽

pp. 4431-4438 ◽

Author(s):

J. Darío Perea ◽

Stefan Langner ◽

Michael Salvador ◽

Janos Kontos ◽

Gabor Jarvas ◽

...

Keyword(s):

Neural Networks ◽

Density Functional Theory ◽

Artificial Neural Networks ◽

Density Functional ◽

Computational Approach ◽

Solubility Parameters ◽

Functional Theory ◽

Artificial Neural

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An efficient rule-based screening approach for discovering fast lithium ion conductors using density functional theory and artificial neural networks

Journal of Materials Chemistry A ◽

10.1039/c3ta13235h ◽

2014 ◽

Vol 2 (3) ◽

pp. 720-734 ◽

Author(s):

Randy Jalem ◽

Masanobu Nakayama ◽

Toshihiro Kasuga

Keyword(s):

Neural Networks ◽

Density Functional Theory ◽

Artificial Neural Networks ◽

Density Functional ◽

Lithium Ion ◽

Functional Theory ◽

Screening Approach ◽

Artificial Neural ◽

Lithium Ion Conductors

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Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using density functional theory and artificial neural networks

The Journal of Chemical Physics ◽

10.1063/1.3369007 ◽

2010 ◽

Vol 132 (17) ◽

pp. 174113 ◽

Author(s):

Tuğba Davran-Candan ◽

M. Erdem Günay ◽

Ramazan Yıldırım

Keyword(s):

Neural Networks ◽

Density Functional Theory ◽

Gold Nanoparticles ◽

Artificial Neural Networks ◽

Density Functional ◽

Activity Relationship ◽

Functional Theory ◽

O2 Adsorption ◽

Artificial Neural ◽

Structure And Activity

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State-of-the-art predictive modeling of hydroxyapatite nanocrystallite size: a hybrid density functional theory and artificial neural networks

Journal of Sol-Gel Science and Technology ◽

10.1007/s10971-019-05113-0 ◽

2019 ◽

Vol 92 (3) ◽

pp. 641-651 ◽

Author(s):

Mohammad Shakiba ◽

Gholam Reza Khayati ◽

Maliheh Zeraati

Keyword(s):

Neural Networks ◽

Density Functional Theory ◽

Artificial Neural Networks ◽

Predictive Modeling ◽

Density Functional ◽

State Of The Art ◽

Hybrid Density Functional Theory ◽

Functional Theory ◽

Nanocrystallite Size ◽

Hybrid Density Functional

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Mechanical properties study for new hypothetical crystalline phases of ReB2: A computational approach using density functional theory

Computational Materials Science ◽

10.1016/j.commatsci.2016.05.032 ◽

2016 ◽

Vol 122 ◽

pp. 240-248 ◽

Author(s):

Marco Marín-Suárez ◽

Mario E. Vélez ◽

Jorge David ◽

Mauricio Arroyave-Franco

Keyword(s):

Mechanical Properties ◽

Density Functional Theory ◽

Density Functional ◽

Computational Approach ◽

Functional Theory ◽

Crystalline Phases

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Modeling palladium surfaces with density functional theory, neural networks and molecular dynamics

Catalysis Today ◽

10.1016/j.cattod.2018.03.045 ◽

2018 ◽

Vol 312 ◽

pp. 132-140 ◽

Author(s):

Tianyu Gao ◽

John R. Kitchin

Keyword(s):

Neural Networks ◽

Molecular Dynamics ◽

Density Functional Theory ◽

Density Functional ◽

Functional Theory

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Ab Initio Computational Approach for Nanophotonics Based on Time-Dependent Density Functional Theory

10.1007/978-3-030-71516-8_4 ◽

2021 ◽

pp. 103-133

Author(s):

Kazuhiro Yabana ◽

Takashi Takeuchi ◽

Mitsuharu Uemoto ◽

Atsushi Yamada ◽

Shunsuke Yamada

Keyword(s):

Density Functional Theory ◽

Ab Initio ◽

Density Functional ◽

Computational Approach ◽

Time Dependent ◽

Functional Theory ◽

Dependent Density

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Silicon potentials investigated using density functional theory fitted neural networks

Journal of Physics Condensed Matter ◽

10.1088/0953-8984/20/28/285219 ◽

2008 ◽

Vol 20 (28) ◽

pp. 285219 ◽

Author(s):

E Sanville ◽

A Bholoa ◽

R Smith ◽

S D Kenny

Keyword(s):

Neural Networks ◽

Density Functional Theory ◽

Density Functional ◽

Functional Theory

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Artificial neural network prediction indicators of density functional theory metal hydride models

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2013.06.138 ◽

2013 ◽

Vol 38 (27) ◽

pp. 11920-11929 ◽

Author(s):

William O. Griffin ◽

Jerry A. Darsey

Keyword(s):

Neural Network ◽

Density Functional Theory ◽

Artificial Neural Network ◽

Metal Hydride ◽

Density Functional ◽

Functional Theory ◽

Neural Network Prediction ◽

Network Prediction ◽

Artificial Neural

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Theoretical investigation of the [1,5]-hydrogen shift in (Z)-1,3-pentadiene with a density functional theory computational approach

Journal of Molecular Structure THEOCHEM ◽

10.1016/s0166-1280(97)00012-2 ◽

1998 ◽

Vol 423 (3) ◽

pp. 189-194 ◽

Author(s):

Branko S Jursic

Keyword(s):

Density Functional Theory ◽

Theoretical Investigation ◽

Density Functional ◽

Computational Approach ◽

Functional Theory ◽

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Investigating the optical properties of BOIMPY dyes using ab initio tools

Physical Chemistry Chemical Physics ◽

10.1039/c7cp01190c ◽

2017 ◽

Vol 19 (16) ◽

pp. 10554-10561 ◽

Author(s):

Boris Le Guennic ◽

Giovanni Scalmani ◽

Michael J. Frisch ◽

Adèle D. Laurent ◽

Denis Jacquemin

Keyword(s):

Density Functional Theory ◽

Spectral Properties ◽

Density Functional ◽

Computational Approach ◽

Time Dependent ◽

Coupled Cluster ◽

Functional Theory ◽

Td Dft ◽

Large Panel ◽

Dependent Density

Using a computational approach combining Time-Dependent Density Functional Theory (TD-DFT) and second-order Coupled Cluster (CC2) approaches, we investigate the spectral properties of a large panel of BOIMPY dyes.

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