Learning Piezoelectric Tensors through Strain-Conditioned Polarization Clusters
, outperforming EATGNN and CGCNN by 32.5% and 52.6%, respectively. The model also supports large-scale screening of unlabeled materials and identifies candidates with strong piezoelectric response, providing a physically grounded route for tensor learning and materials discovery.
Authors
- Hui Li (ORCID: https://orcid.org/0000-0001-9198-3951)
- Yuchong Kang
- Chunlin Yu (ORCID: https://orcid.org/0000-0002-8567-069X)
- Chao Liang
- Junhao Liang
- Jian Yuan
Institutions
- Sun Yat-sen University (CN)
- NIST Center for Neutron Research (US)
- University of Hong Kong (HK)
Publication Details
- Journal
- Nano Letters
- Published
- 2026-05-08
- DOI
- https://doi.org/10.1021/acs.nanolett.6c01295
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- China Postdoctoral Science Foundation
- Natural Science Foundation of Guangdong Province
- Guangdong Science and Technology Department
- Guangzhou Municipal Science and Technology Bureau
- Science and Technology Planning Project of Guangdong Province