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Inverse design of singlet fission materials with uncertainty-controlled genetic optimization

Luca Schaufelberger1, J. Terence Blaskovits1, Ruben Laplaza1,2, Clemence Corminboeuf1,2*, Kjell Jorner3*

1 Ecole polytechnique fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-1015

2 National Center for Competence in Research – Catalysis (NCCR-Catalysis), Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland, CH-1015

3 ETH Zürich, Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 1, Zürich, Switzerland, CH-8093

* Corresponding authors emails: clemence.corminboeuf@epfl.ch, kjell.jorner@chem.ethz.ch
DOI10.24435/materialscloud:yn-vz [version v1]

Publication date: Jul 04, 2024

How to cite this record

Luca Schaufelberger, J. Terence Blaskovits, Ruben Laplaza, Clemence Corminboeuf, Kjell Jorner, Inverse design of singlet fission materials with uncertainty-controlled genetic optimization, Materials Cloud Archive 2024.104 (2024), https://doi.org/10.24435/materialscloud:yn-vz


Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

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File name Size Description
1.9 MiB Folder containing reFORMED (cores and substituents) as well as the uncurated fragments.
417.0 MiB Folder containing the TD-DFT calculations of the external test set.
9.9 MiB Folder containing the TD-DFT calculations of the (pruned) top candidates from the ucGA, as shown in Figure 7.
7.0 MiB Folder containing the TD-DFT calculations of top candidates from the ML screening, as shown in Figure 8.
5.0 KiB Folder containing the xyz files for the diradical analysis.
115.5 MiB CSV file containing the tabulated properties for the FORMED database, including SMILES.
695 Bytes READ_ME


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

L. Schaufelberger J. T. Blaskovits, R. Laplaza, C. Corminboeuf, K. Jorner, ChemRxiv (2024)


singlet fission machine learning uncertainty quantification inverse design genetic algorithm

Version history:

2024.104 (version v1) [This version] Jul 04, 2024 DOI10.24435/materialscloud:yn-vz