Hybridizing physical and data-driven prediction methods for physicochemical properties
Abstract
We present a generic, highly effective approach to combine physical and data-driven prediction methods for physicochemical properties based on Bayesian machine learning and model distillation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2020
- Source ID
- 10.1039/d0cc05258b
Entities
People
- Fabian Jirasek
- Robert Bamler
- Stephan Mandt
Organizations
- Defense Advanced Research Projects Agency
- Department of Computer Science, University of Oxford
- German Academic Exchange Service
- National Science Foundation
- Qualcomm
- United States Army
- University of California
- University of California, Irvine