Oxidation states of binary oxides from data analytics of the electronic structure

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Oxidation states of binary oxides from data analytics of the electronic structure. / Posysaev, Sergei; Miroshnichenko, Olga; Alatalo, Matti; Le, Duy; Rahman, Talat S.

In: Computational Materials Science, Vol. 161, 15.04.2019, p. 403-414.

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Posysaev, Sergei ; Miroshnichenko, Olga ; Alatalo, Matti ; Le, Duy ; Rahman, Talat S. / Oxidation states of binary oxides from data analytics of the electronic structure. In: Computational Materials Science. 2019 ; Vol. 161. pp. 403-414.

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@article{b78369825a3a48e084105839e150dac3,
title = "Oxidation states of binary oxides from data analytics of the electronic structure",
abstract = "Despite their simplicity, the concepts of oxidation states (OS) and Bader charge are widely used in the analysis of experimental data and theoretical calculations for gaining physical and chemical insights. However, a connection between the OS and Bader charge has not been generally demonstrated. To our knowledge, all previous works concerning this connection were limitted to a small number of compounds. The aim of this work was to find a dependency between OS and Bader charge for a large number of compounds from an open database. Using a simple machine learning algorithm, linear regression, we show that a correlation indeed exists for the binary oxide systems. The applicability of determining OS by Bader charges for binary oxides in mixed-valence compounds and surfaces is considered and the limits of validity of the approach is discussed.",
keywords = "Bader charge, Electronic structure, Oxidation state",
author = "Sergei Posysaev and Olga Miroshnichenko and Matti Alatalo and Duy Le and Rahman, {Talat S.}",
year = "2019",
month = "4",
day = "15",
doi = "10.1016/j.commatsci.2019.01.046",
language = "English",
volume = "161",
pages = "403--414",
journal = "Computational Materials Science",
issn = "0927-0256",
publisher = "Elsevier",

}

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TY - JOUR

T1 - Oxidation states of binary oxides from data analytics of the electronic structure

AU - Posysaev, Sergei

AU - Miroshnichenko, Olga

AU - Alatalo, Matti

AU - Le, Duy

AU - Rahman, Talat S.

PY - 2019/4/15

Y1 - 2019/4/15

N2 - Despite their simplicity, the concepts of oxidation states (OS) and Bader charge are widely used in the analysis of experimental data and theoretical calculations for gaining physical and chemical insights. However, a connection between the OS and Bader charge has not been generally demonstrated. To our knowledge, all previous works concerning this connection were limitted to a small number of compounds. The aim of this work was to find a dependency between OS and Bader charge for a large number of compounds from an open database. Using a simple machine learning algorithm, linear regression, we show that a correlation indeed exists for the binary oxide systems. The applicability of determining OS by Bader charges for binary oxides in mixed-valence compounds and surfaces is considered and the limits of validity of the approach is discussed.

AB - Despite their simplicity, the concepts of oxidation states (OS) and Bader charge are widely used in the analysis of experimental data and theoretical calculations for gaining physical and chemical insights. However, a connection between the OS and Bader charge has not been generally demonstrated. To our knowledge, all previous works concerning this connection were limitted to a small number of compounds. The aim of this work was to find a dependency between OS and Bader charge for a large number of compounds from an open database. Using a simple machine learning algorithm, linear regression, we show that a correlation indeed exists for the binary oxide systems. The applicability of determining OS by Bader charges for binary oxides in mixed-valence compounds and surfaces is considered and the limits of validity of the approach is discussed.

KW - Bader charge

KW - Electronic structure

KW - Oxidation state

UR - http://www.scopus.com/inward/record.url?scp=85062262740&partnerID=8YFLogxK

U2 - 10.1016/j.commatsci.2019.01.046

DO - 10.1016/j.commatsci.2019.01.046

M3 - Article

VL - 161

SP - 403

EP - 414

JO - Computational Materials Science

JF - Computational Materials Science

SN - 0927-0256

ER -

ID: 32502561