Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
Abstract
Fine-tuned MatterGen diffusion-model checkpoints accompanying the paper An Environment-Conditioned Generative Model Designs Inert Anodes across Opposite Molten-Salt Chemistries. Three fifteen-property fine-tunes of the public MatterGen base checkpoint, trained on 30,161 transition-metal oxides from Alex-MP-20 with identical architecture and hyperparameters. They differ only in the per-cation anodic-stability table used to label the bath-aware and weakest-link corrosion properties: Fluoride model, first generation (LiF–NdF3–Nd2O3; 200,000-candidate main campaign) Fluoride model, second generation (same bath, Mn and Al scores recalibrated; Ni2+ high-entropy campaign) Chloride model (CaCl2–CaF2–CaO, calcium electro-reduction) Each model is provided as the final PyTorch Lightning checkpoint (*_last.ckpt) with its training configuration (*_config.yaml). See README.md for the directory layout MatterGen expects and SHA256SUMS for checksums. Code: https://github.com/jonglee69/OxideMatterGen
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