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Chen, Ke ; Kunkel, Christian ; Reuter, Karsten ; Margraf, Johannes T.:
Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening.
In: Digital Discovery.
Bd. 1
(2022)
Heft 2
.
- S. 147-157.
ISSN 2635-098X
DOI: https://doi.org/10.1039/D1DD00038A
Abstract
The molecular reorganization energy λ strongly influences the charge carrier mobility of organic semiconductors and is therefore an important target for molecular design. Machine learning (ML) models generally have the potential to strongly accelerate this design process (e.g. in virtual screening studies) by providing fast and accurate estimates of molecular properties. While such models are well established for simple properties (e.g. the atomization energy), λ poses a significant challenge in this context. In this paper, we address the questions of how ML models for λ can be improved and what their benefit is in high-throughput virtual screening (HTVS) studies. We find that, while improved predictive accuracy can be obtained relative to a semiempirical baseline model, the improvement in molecular discovery is somewhat marginal. In particular, the ML enhanced screenings are more effective in identifying promising candidates but lead to a less diverse sample. We further use substructure analysis to derive a general design rule for organic molecules with low λ from the HTVS results.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Chemie > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Chemie > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Lehrstuhl Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 540 Chemie |
| Eingestellt am: | 13 Nov 2023 13:59 |
| Letzte Änderung: | 13 Nov 2023 13:59 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/87654 |