Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

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Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm. / Ree, Nicolai; Koerstz, Mads; Mikkelsen, Kurt V.; Jensen, Jan H.

I: Journal of Chemical Physics, Bind 155, Nr. 18, 184105, 14.11.2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Ree, N, Koerstz, M, Mikkelsen, KV & Jensen, JH 2021, 'Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm', Journal of Chemical Physics, bind 155, nr. 18, 184105. https://doi.org/10.1063/5.0063694

APA

Ree, N., Koerstz, M., Mikkelsen, K. V., & Jensen, J. H. (2021). Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm. Journal of Chemical Physics, 155(18), [184105]. https://doi.org/10.1063/5.0063694

Vancouver

Ree N, Koerstz M, Mikkelsen KV, Jensen JH. Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm. Journal of Chemical Physics. 2021 nov. 14;155(18). 184105. https://doi.org/10.1063/5.0063694

Author

Ree, Nicolai ; Koerstz, Mads ; Mikkelsen, Kurt V. ; Jensen, Jan H. / Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm. I: Journal of Chemical Physics. 2021 ; Bind 155, Nr. 18.

Bibtex

@article{30f23338f2d6420db2e1db5361f5be2e,

title = "Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm",

abstract = "We present a computational methodology for the screening of a chemical space of 1025 substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34 000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg, and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg. ",

author = "Nicolai Ree and Mads Koerstz and Mikkelsen, {Kurt V.} and Jensen, {Jan H.}",

note = "Publisher Copyright: {\textcopyright} 2021 Author(s).",

year = "2021",

month = nov,

day = "14",

doi = "10.1063/5.0063694",

language = "English",

volume = "155",

journal = "The Journal of Chemical Physics",

issn = "0021-9606",

publisher = "American Institute of Physics",

number = "18",

}

RIS

TY - JOUR

T1 - Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

AU - Ree, Nicolai

AU - Koerstz, Mads

AU - Mikkelsen, Kurt V.

AU - Jensen, Jan H.

PY - 2021/11/14

Y1 - 2021/11/14

N2 - We present a computational methodology for the screening of a chemical space of 1025 substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34 000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg, and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg.

AB - We present a computational methodology for the screening of a chemical space of 1025 substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34 000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg, and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg.

U2 - 10.1063/5.0063694

DO - 10.1063/5.0063694

M3 - Journal article

C2 - 34773961

AN - SCOPUS:85119072538

VL - 155

JO - The Journal of Chemical Physics

JF - The Journal of Chemical Physics

SN - 0021-9606

IS - 18

M1 - 184105

ER -

ID: 285306996

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