Downloads · 30 days
0
Selvauma/na-air-deepmd
na-air-deepmd is a machine learning model from Selvauma. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.
Downloads · 30 days
0
Access
Public
Updated Sep 11, 2026
Repo size
27.7 MB
Likes
0
Public
Click a slice to open those files.
.pb27.5 MB · 99%
From the Hugging Face model README

A single DeepMD-kit potential covering Na-air battery discharge-product phases: Na₂O, Na₂O₂, NaO₂, and their mixed-phase interfaces (Na₂O–Na₂O₂, Na₂O₂–NaO₂, and the three-phase Na₂O–Na₂O₂–NaO₂ junction), trained across a shared composition space so the same model covers pure phases and their interfaces together (7 temperatures, 300–380K, per phase/interface).
Na-air (Na-O₂) batteries discharge by depositing one of these three sodium oxide phases at the cathode; which phase (and how it interconverts at phase boundaries) governs rechargeability and overpotential — hence training one potential across all of them together rather than phase-by-phase.
D (Na, AIMD) = 2.3×10⁻¹⁰ cm²/s, Eₐ = 0.21 eV · D (MLMD) = 2.5×10⁻¹⁰ cm²/s, Eₐ = 0.22 eV. Mechanical: E = 15 GPa, ν = 0.30, ρ = 2.2 g/cm³.
| File | RMSE energy (eV/atom) | RMSE force (eV/Å) | Training steps | Size |
|---|---|---|---|---|
model/na_air_production.pb | — | — | — | 27.5 MB |
RMSE values are validation-set (held-out) energy/force error, read directly from each run's DeepMD-kit lcurve.out at its final training step — not re-derived or estimated.
One or more files have no lcurve.out preserved on disk (training log not kept) — size/provenance is real, but no quantitative accuracy number can be reported for those.
AIMD (VASP, PBE) across all phase/interface configurations → DeepMD-kit train/freeze/compress. Produced by HPCA (github.com/selvachandrasekaranselvaraj/hpca).
Selva Chandrasekaran Selvaraj, University of Illinois Chicago.