Quantum-Chemical Simulation of Multiresonance Thermally Activated Delayed Fluorescence Materials Based on B,N-Heteroarenes Using Graph Neural Networks

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Quantum-Chemical Simulation of Multiresonance Thermally Activated Delayed Fluorescence Materials Based on B,N-Heteroarenes Using Graph Neural Networks. / Tarakanovskaya, Darya D.; Mostovich, Evgeny A.

In: Journal of Physical Chemistry A, 08.05.2025.

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

BibTeX

@article{6c2a44f70af443519b7cc768478e3e51,

title = "Quantum-Chemical Simulation of Multiresonance Thermally Activated Delayed Fluorescence Materials Based on B,N-Heteroarenes Using Graph Neural Networks",

abstract = "Multiresonance thermally activated delayed fluorescence (MR-TADF) emitters are crucial for the next generation of electroluminescent devices due to their high efficiency and narrowband emission. In this study, we developed a simple molecular design for MR-TADF materials based on a π-extended DABNA core decorated with four different framework types (carbazole (X = none), acridine (X = C(Me)2), phenoxazine (X = O), and phenothiazine (X = S)) and further modified with 18 different annulated systems. The optoelectronic properties of these compounds were modeled using density functional theory. Based on quantum chemical calculations, an accelerated search tool for MR-TADF emitters was developed using deep learning methods, enabling the prediction of energy values approximating experimental results.",

keywords = "Molecules, Oscillation, Aromatic compounds, Chemical structure, Molecular structure",

author = "Tarakanovskaya, {Darya D.} and Mostovich, {Evgeny A.}",

note = "This work was supported by the Ministry of Science and Higher Education of the Russian Federation, project No FSUS-2021-0014. The authors are grateful to the Supercomputing Centre of the Novosibirsk State University for providing computational resources ( http://nusc.nsu.ru/ ). ",

year = "2025",

month = may,

day = "8",

doi = "10.1021/acs.jpca.5c01243",

language = "English",

journal = "Journal of Physical Chemistry A",

issn = "1089-5639",

publisher = "ACS Publication",

}

RIS

TY - JOUR

T1 - Quantum-Chemical Simulation of Multiresonance Thermally Activated Delayed Fluorescence Materials Based on B,N-Heteroarenes Using Graph Neural Networks

AU - Tarakanovskaya, Darya D.

AU - Mostovich, Evgeny A.

N1 - This work was supported by the Ministry of Science and Higher Education of the Russian Federation, project No FSUS-2021-0014. The authors are grateful to the Supercomputing Centre of the Novosibirsk State University for providing computational resources ( http://nusc.nsu.ru/ ).

PY - 2025/5/8

Y1 - 2025/5/8

N2 - Multiresonance thermally activated delayed fluorescence (MR-TADF) emitters are crucial for the next generation of electroluminescent devices due to their high efficiency and narrowband emission. In this study, we developed a simple molecular design for MR-TADF materials based on a π-extended DABNA core decorated with four different framework types (carbazole (X = none), acridine (X = C(Me)2), phenoxazine (X = O), and phenothiazine (X = S)) and further modified with 18 different annulated systems. The optoelectronic properties of these compounds were modeled using density functional theory. Based on quantum chemical calculations, an accelerated search tool for MR-TADF emitters was developed using deep learning methods, enabling the prediction of energy values approximating experimental results.

AB - Multiresonance thermally activated delayed fluorescence (MR-TADF) emitters are crucial for the next generation of electroluminescent devices due to their high efficiency and narrowband emission. In this study, we developed a simple molecular design for MR-TADF materials based on a π-extended DABNA core decorated with four different framework types (carbazole (X = none), acridine (X = C(Me)2), phenoxazine (X = O), and phenothiazine (X = S)) and further modified with 18 different annulated systems. The optoelectronic properties of these compounds were modeled using density functional theory. Based on quantum chemical calculations, an accelerated search tool for MR-TADF emitters was developed using deep learning methods, enabling the prediction of energy values approximating experimental results.

KW - Molecules

KW - Oscillation

KW - Aromatic compounds

KW - Chemical structure

KW - Molecular structure

UR - https://www.mendeley.com/catalogue/2eb5763b-b0bc-3760-8589-c18b26123204/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-105004750997&origin=inward&txGid=ff2894c7008328d32a1083e85a6165f9

U2 - 10.1021/acs.jpca.5c01243

DO - 10.1021/acs.jpca.5c01243

M3 - Article

C2 - 40338523

JO - Journal of Physical Chemistry A

JF - Journal of Physical Chemistry A

SN - 1089-5639

ER -

ID: 66502724