[SUPERSEDED] The Materials-Discovery Pipeline - Activation as a First-Class Filter
Year: 2026
Type: preprint
Abstract: This record has been superseded. The current, updated version is A Machine-Learning Interatomic-Potential to Activation-Transport Pipeline for Low-Activation Fusion Alloy Discovery: CHGNet Screening, DFT Confirmation, and OpenMC/FISPACT Down-Selection (DOI 10.5281/zenodo.22645797). Please read and cite the current version. This page stays online so existing citations keep resolving. 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.
Source: OSF Preprints (OSF Preprints)
Author: Ford, P. I.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204564360