Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks
We develop a physics-informed machine learning workflow that accelerates multicomponent phase-coexistence calculations on the number, composition, and abundance of phases. The workflow is demonstrated for systems described by Flory–Huggins theory.
Authors
- Shengli Jiang (ORCID: https://orcid.org/0000-0002-5421-5803)
- Michael A. Webb (ORCID: https://orcid.org/0000-0002-7420-4474)
- Satyen Dhamankar
Institutions
- Princeton University (US)
Publication Details
- Journal
- Molecular Systems Design & Engineering
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1039/d4me00168k
- Citations
- 7
- Primary Topic
- Quantum, superfluid, helium dynamics
- Type
- article
- Field-Weighted Citation Impact
- 2.36
Funders
- National Science Foundation
- Princeton University
- Division of Materials Research