The Materials-Discovery Pipeline - Activation as a First-Class Filter
Abstract
The method behind the low-activation alloy family: a three-stage GPU pipeline — a universal ML interatomic potential (CHGNet) screens thousands of compositions by formation energy, density-functional theory confirms the survivors, and an activation-transport down-select (OpenMC→FISPACT) rejects any whose waste class or decay heat is unacceptable. The novelty is making activation a first-class filter, early.
Fields of Study & Subjects
Machine Learning in Materials Science
Inorganic Chemistry and Materials
Electronic and Structural Properties of Oxides
Publication details
- Journal / source
- OSF Preprints (OSF Preprints)
- Publication date
- 28 August 2026
- Language
- EN
- Document type
- Preprint