Standard

Crystal Structure Representation for Neural Networks using Topological Approach. / Fedorov, Aleksandr V.; Shamanaev, Ivan V.

In: Molecular Informatics, Vol. 36, No. 8, 1600162, 08.2017.

Research output: Contribution to journal › Article › peer-review

Harvard

Fedorov, AV & Shamanaev, IV 2017, 'Crystal Structure Representation for Neural Networks using Topological Approach', Molecular Informatics, vol. 36, no. 8, 1600162. https://doi.org/10.1002/minf.201600162

APA

Fedorov, A. V., & Shamanaev, I. V. (2017). Crystal Structure Representation for Neural Networks using Topological Approach. Molecular Informatics, 36(8), [1600162]. https://doi.org/10.1002/minf.201600162

Vancouver

Fedorov AV, Shamanaev IV. Crystal Structure Representation for Neural Networks using Topological Approach. Molecular Informatics. 2017 Aug;36(8):1600162. doi: 10.1002/minf.201600162

Author

Fedorov, Aleksandr V. ; Shamanaev, Ivan V. / Crystal Structure Representation for Neural Networks using Topological Approach. In: Molecular Informatics. 2017 ; Vol. 36, No. 8.

BibTeX

@article{4869ebabb0314ab89812b8c7acb619f5,
title = "Crystal Structure Representation for Neural Networks using Topological Approach",
abstract = "In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %.",
keywords = "artificial neural network, entropy, lattice energy, molar heat capacity, ToposPro, GRAPH, DESIGN, LATTICE ENERGIES, PREDICTION, SPACE, MATERIALS INFORMATICS, SOLIDS, INORGANIC-COMPOUNDS, ENERGY ESTIMATION",
author = "Fedorov, {Aleksandr V.} and Shamanaev, {Ivan V.}",
year = "2017",
month = aug,
doi = "10.1002/minf.201600162",
language = "English",
volume = "36",
journal = "Molecular Informatics",
issn = "1868-1743",
publisher = "Wiley - VCH Verlag GmbH & CO. KGaA",
number = "8",

}

RIS

TY - JOUR

T1 - Crystal Structure Representation for Neural Networks using Topological Approach

AU - Fedorov, Aleksandr V.

AU - Shamanaev, Ivan V.

PY - 2017/8

Y1 - 2017/8

N2 - In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %.

AB - In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %.

KW - artificial neural network

KW - entropy

KW - lattice energy

KW - molar heat capacity

KW - ToposPro

KW - GRAPH

KW - DESIGN

KW - LATTICE ENERGIES

KW - PREDICTION

KW - SPACE

KW - MATERIALS INFORMATICS

KW - SOLIDS

KW - INORGANIC-COMPOUNDS

KW - ENERGY ESTIMATION

UR - http://www.scopus.com/inward/record.url?scp=85014617589&partnerID=8YFLogxK

U2 - 10.1002/minf.201600162

DO - 10.1002/minf.201600162

M3 - Article

AN - SCOPUS:85014617589

VL - 36

JO - Molecular Informatics

JF - Molecular Informatics

SN - 1868-1743

IS - 8

M1 - 1600162

ER -

ID: 10276210