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Selvauma/srf2-deepmd
srf2-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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.pb354 MB · 100%
From the Hugging Face model README

DeepMD-kit potentials for SrF₂, studied as an F⁻-conducting protective coating on Li-metal anodes. Unlike the other families here, the mobile species of interest is the fluoride anion, not Li⁺ — the coating's job is to be an F⁻ conductor that physically/chemically shields the Li-metal surface.
| AIMD | MLMD (DeepMD) | |
|---|---|---|
| D (F⁻, cm²/s) | 4.8×10⁻¹¹ | 5.0×10⁻¹¹ |
| Eₐ (eV) | 0.57 | 0.58 |
Mechanical: E = 88 GPa, ν = 0.28, Ω = 19.5 ų/formula unit, ρ = 4.24 g/cm³.
| File | RMSE energy (eV/atom) | RMSE force (eV/Å) | Training steps | Size |
|---|---|---|---|---|
model/srf2_aimd_run1.pb | 0.00363 | 0.0865 | 1,000,000 | 207.1 MB |
model/srf2_aimd_run2.pb | 0.00608 | 0.0999 | 1,000,000 | 146.6 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.

2 of 5 distinct potentials, both independent training runs against the
same AIMD trajectory set (srf2_aimd_run1/run2) — selected because they're
the only two with a preserved lcurve.out to validate against. The
large-scale production potential actually deployed for the multi-nanosecond
NVT/NPT interface runs (deepmd/n_opt/mlpot.pb) has no training log kept on
disk, so it's excluded from this metrics-first selection — ask if you want
it included anyway (unvalidated) or swapped in for one of these two. Several
older potentials from earlier iterations of this system are archived/
superseded and also not included.
AIMD (VASP, PBE) → DeepMD-kit train → freeze → compress. Produced by HPCA (github.com/selvachandrasekaranselvaraj/hpca).
Selva Chandrasekaran Selvaraj, University of Illinois Chicago.