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Selvauma/sses-coating-deepmd
sses-coating-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.
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Updated Sep 11, 2026
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From the Hugging Face model README

2 of 5 DeepMD-kit potentials from a comparative screen of candidate protective coating materials for a Li-metal anode interface: AlF₃, MgF₂, Al₂O₃, SbF₃, ZnCl₂. Each was trained on AIMD data for that coating's Li-metal interface slab (300/1000/3000 K sampling), then used for longer MLMD screening across 300–450 K. The two included here (ZnCl₂, MgF₂) are the two with the lowest validation force RMSE of the five — not necessarily the two best coatings scientifically (AlF₃ is the current transport front-runner in this project; ask if you want it swapped in instead).
This is a comparative screen, not one material — the point is to rank candidates against each other on the same footing (same interface construction, same training/validation protocol) before committing to one for deeper study.
| File | RMSE energy (eV/atom) | RMSE force (eV/Å) | Training steps | Size |
|---|---|---|---|---|
model/ZnCl2_Li.pb | 0.00435 | 0.0747 | 1,000,000 | 73.0 MB |
model/MgF2_Li.pb | 0.00573 | 0.103 | 1,000,000 | 58.9 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.

Al, Mg, Sb, Zn (coating cations) · F, Cl, O (coating anions) · Li (mobile ion, shared framework across all five).
AIMD interface slabs (VASP, PBE, 300/1000/3000 K) → DeepMD-kit train/freeze/ compress → MLMD screening (300–450 K, 7 temperatures). Produced by HPCA (github.com/selvachandrasekaranselvaraj/hpca).
Selva Chandrasekaran Selvaraj, University of Illinois Chicago.