| name | mace-ni-benchmark |
| description | Use when the user asks to reproduce the Kreitz 2021 Ni surface benchmark, run the MACE Ni benchmark, or compare a machine-learning potential (MACE, CHGNet, M3GNet) against DFT-D3 surface-science references. Invokes the "UMA Catalysis Tutorial" preset (template key `uma_catalysis_screening`).
|
MACE Ni Benchmark — Kreitz 2021 Reproduction
Reproduces six Ni surface DFT-D3 target quantities end-to-end with
MACE-MP-0, on a single Ni bulk source, in one workflow.
When to invoke
Trigger phrases (any of):
- "reproduce Kreitz 2021 Ni benchmark"
- "run the MACE Ni benchmark"
- "benchmark MACE against DFT-D3 for Ni"
- "validate MLP on Ni surfaces"
Also invoke when the user asks to compute multiple of the six quantities
below for Ni at once — one preset is cheaper than six separate workflows.
The six target quantities
| # | Quantity | Source node | Result key |
|---|
| 1 | γ(111), γ(100), γ(110), γ(211) | surface_energy | per_facet[hkl].gamma_J_per_m2 |
| 2 | Wulff facet area fractions | wulff_construction | area_fractions[hkl] |
| 3 | H adsorption energy on Ni(111) FCC hollow (ZPE-corrected) | adsorption_energy | E_ads_ZPE_eV |
| 4 | Coverage slope ∂E_ads/∂θ (1,2,4,8,16 H on 4×4 Ni(111)) | coverage_analysis | fit.slope |
| 5 | CO* ↔ C* + O* NEB barrier | ts_search (mlp_neb) | activation_barrier_kcal_mol |
| 6 | TS imaginary-mode frequency | freq (mlp_vibrations) | dominant_imag_freq_cm (with is_valid_ts flag) |
All six are viewable side-by-side in the project dashboard's "Benchmark"
tab once any workflow derived from uma_catalysis_screening (or with a
matching name) is present in the project.
How to invoke
Option A — UI (recommended)
In the Workflow Editor, click New from preset → Surface Catalysis →
UMA Catalysis Tutorial. A 26-node DAG loads. The template is defined
in src/lib/workflow/graph-model.ts::uma_catalysis_screening.
After it loads, the user must load structures into 4 input nodes:
Ni bulk (FCC) — fcc Ni, a ≈ 3.524 Å (Materials Project mp-23)H₂ molecule — two H atoms ~0.74 Å apart in a 20 Å boxCO* on Ni(111) — NEB reactant (CO adsorbed on a 3×3 Ni(111) slab)C* + O* on Ni(111) — NEB product (C and O separately adsorbed)
Option B — Manual DAG build (not recommended)
Rebuilding the 26-node DAG by hand costs ~2x the effort and always drifts
from the defaults tested against MACE-MP-0 medium. Only do this if the
user needs a custom variant (e.g. different slab supercell, or a
non-cubic/non-Ni system).
Expected deviations (MACE-MP-0 vs RPBE-D3)
| Quantity | Typical |CatGo − Kreitz| | Notes |
|---|
| γ(hkl) | ~0.1 J/m² | γ(111) tends to be ~0.05 J/m² higher |
| Wulff fractions | < 0.05 | Dominant (111) facet rank is preserved |
| E_ads(H, ZPE) | ~0.1 eV | MACE-MP-0 slightly overbinds H |
| Coverage slope | ~0.03 eV/ML | Sign (repulsive) should match |
| NEB barrier | ~0.2 eV | Largest single deviation |
| ν_imag | ~50 cm⁻¹ | Sign must be negative (imaginary) |
If deviations are much larger than these ranges, check:
- Did the bulk opt converge? (
fmax < 0.05 eV/Å with relax_cell: true)
- Did NEB converge to the expected CI image? (
neb_converged: true)
- Is
is_valid_ts: true on the freq step at the TS? (Exactly one
imaginary mode above the 20 cm⁻¹ trivial-mode filter.)
Defaults worth preserving
software: mlp, model: MACE, device: auto → uses MACE-MP-0 medium
via the default mace_mp("medium", default_dtype="float64") path.
Checkpoint auto-downloads to ~/.cache/mace/ on first run
(~200 MB, ~2 min).- Vibrations freeze the Ni slab and vibrate the adsorbate only
(
freeze_mode: layers, freeze_layers: 2, freeze_invert: false) →
~20× cheaper freqs without losing ZPE accuracy. Note: freeze_invert
inverts the set of atoms ASE displaces, so false here means the
frozen set (bottom 2 Ni layers) is actually frozen and everything
else vibrates — the standard catalysis setup. true would vibrate
only the bottom 2 Ni layers (wrong for ZPE).
- NEB: 8 images,
climb: true, FIRE optimizer, fmax: 0.05 eV/Å.
- Coverage sweep: 1,2,4,8,16 H on 4×4 hollow-site filling.
Reproducibility
Every MLP-dispatched step writes metadata.json (captured via the C1
footer in server/workflow/engines/mlp.py) into result_json.metadata:
{
"mace_torch_version": "0.3.15",
"torch_version": "2.10.0",
"mace_model": "mace-mp-0-medium",
"model_sha256": null,
"device": "cuda:0" | "cpu",
"gpu_name": "...",
"wall_time_s": 12.3,
"host": "...",
"timestamp": "..."
}
The Benchmark tab surfaces the latest MLP step's metadata panel. Users
export CSV from the same tab to share the full 6-row table with the
metadata footer included as RFC-4180-escaped comment lines.
Related skills
structure/slab/ — slab generation internalsadsorption/ — the general E_ads formula this preset specializesoer/, her/ — if the user wants surface reactivity trends on top of γ(hkl)
