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
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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.
N1 - Publisher Copyright: © 2021 Author(s).
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