Probing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High-Energy Compounds

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dc.authoridMerinov, Valera/0000-0002-8755-4318
dc.authoridKochaev, Aleksey/0000-0002-3521-5891
dc.authoridMaslov, Mikhail/0000-0001-8498-4817
dc.authoridKatin, Konstantin/0000-0003-0225-5712
dc.contributor.authorBondarev, Nikolay, V
dc.contributor.authorKatin, Konstantin P.
dc.contributor.authorMerinov, Valeriy B.
dc.contributor.authorKochaev, Alexey, I
dc.contributor.authorKaya, Savas
dc.contributor.authorMaslov, Mikhail M.
dc.date.accessioned2024-10-26T18:09:54Z
dc.date.available2024-10-26T18:09:54Z
dc.date.issued2022
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe relationships between the theoretical values calculated using density functional theory and experimental data derived from the differential scanning calorimetry of high-energy organic compounds are studied. The theoretical values are the number of atoms and bonds of different types and their lengths, minimum eigenfrequencies, atomization energies, ionization potentials, electron affinities, and frontier orbital energies. The experimental data are the amounts of releasing heat (the first peaks higher than 1 kJ g(-1)) and corresponding temperatures. Neural networks and regression, factor, discriminant, and cluster analysis are applied to find the dependencies between theoretical values and experimental data. It is found that the heat amount cannot be predicted in the general cases, whereas the corresponding temperature can be predicted with a neural network with an accuracy of approximate to 30 degrees C. Cluster and discriminant analysis provides the way for the classification of high-energy compounds into three groups. Some of these groups require particular rules for the prediction of experimental data from the theoretical values.
dc.description.sponsorshipRussian Federation [MK-722.2020.2]
dc.description.sponsorshipThe reported study was financially supported by the Grant of the President of Russian Federation No. MK-722.2020.2.
dc.identifier.doi10.1002/pssr.202100191
dc.identifier.issn1862-6254
dc.identifier.issn1862-6270
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85109258558
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1002/pssr.202100191
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30332
dc.identifier.volume16
dc.identifier.wosWOS:000668861300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-V C H Verlag Gmbh
dc.relation.ispartofPhysica Status Solidi-Rapid Research Letters
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectdensity functional theory
dc.subjecthigh-energy-density materials
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectquantum chemistry descriptors
dc.titleProbing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High-Energy Compounds
dc.typeArticle

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