Carbon Fluoride, CFx: Structural Diversity as Predicted by First Principles

C. Goyenola; S. Stafström; S. Schmidt; L. Hultman; G. K. Gueorguiev

doi:10.1021/jp500653c

Carbon Fluoride, CFx: Structural Diversity as Predicted by First Principles

The Journal of Physical Chemistry C ◽

10.1021/jp500653c ◽

2014 ◽

Vol 118 (12) ◽

pp. 6514-6521 ◽

Cited By ~ 28

Author(s):

C. Goyenola ◽

S. Stafström ◽

S. Schmidt ◽

L. Hultman ◽

G. K. Gueorguiev

Keyword(s):

First Principles ◽

Structural Diversity ◽

Carbon Fluoride

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Adsorption of sulfur mustard on clean and water-saturated ZnO(101¯0): Structural diversity from first-principles calculations

Journal of Hazardous Materials ◽

10.1016/j.jhazmat.2020.123503 ◽

2021 ◽

Vol 402 ◽

pp. 123503

Author(s):

Tobias Klöffel ◽

Mariana Kozlowska ◽

Stanislaw Popiel ◽

Bernd Meyer ◽

Pawel Rodziewicz

Keyword(s):

First Principles ◽

Sulfur Mustard ◽

Structural Diversity ◽

First Principles Calculations ◽

Water Saturated

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Construction of a series of zero-dimensional/one-dimensional crystalline Zn–S clusters – effect of the character of bridging organic ligands on structural diversity

Inorganic Chemistry Frontiers ◽

10.1039/c4qi00227j ◽

2015 ◽

Vol 2 (2) ◽

pp. 164-169 ◽

Cited By ~ 5

Author(s):

Xianghua Zeng ◽

Xiaojing Yao ◽

Junyong Zhang ◽

Qi Zhang ◽

Wenqian Wu ◽

...

Keyword(s):

First Principles ◽

Structural Diversity ◽

Helical Structure ◽

Organic Ligands ◽

First Principles Calculations ◽

One Dimensional

A series of Zn–S clusters have been synthesised and a very rare 1D helical structure was illustrated by first-principles calculations.

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Origin of the structural diversity of the alkaline metal borohydride MBH4 (M = Li, Na, K, Rb and Cs): Insights from first-principles calculations

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2020.02.015 ◽

2020 ◽

Vol 45 (16) ◽

pp. 9946-9958

Author(s):

Qinfu Zhao ◽

Qi Song ◽

Haiyan Zhu ◽

Zhiyong Zhang ◽

Zhenyi Jiang ◽

...

Keyword(s):

First Principles ◽

Structural Diversity ◽

First Principles Calculations ◽

Alkaline Metal

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Stacked Ensemble Machine Learning for Range-Separation Parameters

10.33774/chemrxiv-2021-68fbm-v2 ◽

2021 ◽

Author(s):

Cheng-Wei Ju ◽

Ethan French ◽

Nadav Geva ◽

Alexander Kohn ◽

Zhou Lin

Keyword(s):

Machine Learning ◽

First Principles ◽

Density Functional ◽

Computational Cost ◽

Structural Diversity ◽

Absolute Error ◽

Hartree Fock ◽

Separation Parameter ◽

Ensemble Machine Learning ◽

Organic Semiconducting Molecules

High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange-correlation functionals were developed. The accurate but expensive �first-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree-Fock exchange at a distance characterized by the inverse of a molecule-specific, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning (SEML) model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a_0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved comparable predictive powers in various optical properties.

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