KrononosFE/kronos-2026-2-38-mlip-discovery-pipeline
The Materials-Discovery Pipeline - Activation as a First-Class Filter Authors: Ford, P. I. Summary 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… See the full description on the dataset page: https://huggingface.co/datasets/KrononosFE/kronos-2026-2-38-mlip-discovery-pipeline.
This repository belongs to KrononosFE on Hugging Face.
CoolFace never edits a repository it does not host. Visibility, licence, collaborators and gating are all managed at the source.
