The Materials-Discovery Pipeline - Activation as a First-Class Filter

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-28
DOI
https://doi.org/10.5281/zenodo.22132228
Primary Topic
Machine Learning in Materials Science
Type
preprint
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preprint

The Materials-Discovery Pipeline - Activation as a First-Class Filter

Priyanca Ford
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
preprint

The Materials-Discovery Pipeline - Activation as a First-Class Filter

Priyanca Ford
preprint en

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.

Zenodo (CERN European Organization for Nuclear Research)
Fusion for Energy (ES)
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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The Materials-Discovery Pipeline - Activation as a First-Class Filter — Priyanca Ford · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS