Integrating machine learning and generative models for the intelligent design of TADF materials with circularly polarized luminescence
We propose a machine learning and generative approach for circularly polarized TADF. A robust model finds key g lum features, and a VAE generates candidates with high g lum and good synthetic accessibility for chiral optoelectronics.
Authors
- Pengcheng Jiang (ORCID: https://orcid.org/0000-0001-9989-411X)
- Zhiqin Liang (ORCID: https://orcid.org/0009-0006-2921-8312)
- Bo Qiao (ORCID: https://orcid.org/0000-0002-4416-7764)
- Dandan Song (ORCID: https://orcid.org/0000-0002-5050-8211)
- Yi-Ming Shi
- Haochen Shi (ORCID: https://orcid.org/0000-0002-5641-1517)
Institutions
- Beijing Jiaotong University (CN)
- Hebei Normal University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry C
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5tc02451j
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04