Active and transfer learning with partially Bayesian neural networks for materials and chemicals
This work demonstrates the applicability of partially Bayesian neural networks (PBNNs) to active and transfer learning within materials science and chemistry domains, combining reliable uncertainty quantification (UQ) with NN flexibility.
Authors
- Maxim Ziatdinov (ORCID: https://orcid.org/0000-0003-2570-4592)
- Sarah I. Allec (ORCID: https://orcid.org/0000-0002-1101-3160)
Institutions
- Pacific Northwest National Laboratory (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00027k
- Citations
- 3
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- U.S. Department of Energy
- Battelle
- Pacific Northwest National Laboratory