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license: cc-by-4.0 # TODO: confirm — placeholder, common choice for open MLIP releases (e.g. MACE-OFF/MACE-MP). Change if you want something else.
tags:
- deepmd-kit
- molecular-dynamics
- interatomic-potential
- materials-science
- battery-materials
- fluoride-electrolyte
- coating
- lithium-metal-anode
- anion-conductor
---
# SrF₂ — Li-Metal Anode Protective Coating (DeepMD)
## What this is
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.
## Transport properties (from this project's own AIMD/MLMD)
| | 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.
## Files & Validation
| 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superseded and also not included.
## Training pipeline
AIMD (VASP, PBE) → DeepMD-kit train → freeze → compress. Produced by HPCA
(github.com/selvachandrasekaranselvaraj/hpca).
## Citation
Selva Chandrasekaran Selvaraj, University of Illinois Chicago.
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