Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning
To accelerate materials discovery, we use machine learning models trained on many-body perturbation theory data to predict device-relevant excited-state properties of crystalline organic semiconductors.
Authors
- Noa Marom (ORCID: https://orcid.org/0000-0002-1508-1312)
- Xingyu Liu (ORCID: https://orcid.org/0000-0001-9736-3948)
- Yiqun Luo (ORCID: https://orcid.org/0000-0002-9639-4045)
- Siyu Gao (ORCID: https://orcid.org/0000-0003-4829-5605)
Institutions
- Carnegie Mellon University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00396a
- Citations
- 5
- Primary Topic
- Organic Electronics and Photovoltaics
- Type
- article
- Field-Weighted Citation Impact
- 3.65
Funders
- National Science Foundation
- U.S. Department of Energy
- Office of Science
- Division of Materials Research
- Argonne National Laboratory