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step int64 0 200k | ps float64 0 200 | epot float64 -1,420.57 -1,288.24 | ekin float64 3.26 12.5 | T float64 225 736 |
|---|---|---|---|---|
0 | 0 | -1,308.109213 | 6.096028 | 421.080281 |
50 | 0.05 | -1,307.307245 | 5.60463 | 387.13719 |
100 | 0.1 | -1,306.233913 | 4.693317 | 324.18871 |
150 | 0.15 | -1,307.993603 | 6.966659 | 481.218746 |
200 | 0.2 | -1,308.096169 | 6.855046 | 473.509119 |
250 | 0.25 | -1,308.019821 | 6.799498 | 469.672199 |
300 | 0.3 | -1,308.236457 | 7.060912 | 487.729251 |
350 | 0.35 | -1,308.166632 | 7.580249 | 523.602213 |
400 | 0.4 | -1,307.465144 | 6.729613 | 464.844896 |
450 | 0.45 | -1,308.710279 | 7.859736 | 542.907642 |
500 | 0.5 | -1,308.204916 | 7.719942 | 533.251407 |
550 | 0.55 | -1,308.820272 | 8.305167 | 573.675576 |
600 | 0.6 | -1,307.711961 | 7.3307 | 506.364698 |
650 | 0.65 | -1,308.914125 | 8.518338 | 588.400269 |
700 | 0.7 | -1,308.486844 | 7.53989 | 520.814405 |
750 | 0.75 | -1,308.243349 | 7.171425 | 495.362832 |
800 | 0.8 | -1,308.285801 | 7.344206 | 507.297634 |
850 | 0.85 | -1,307.864357 | 6.58935 | 455.156271 |
900 | 0.9 | -1,308.07301 | 6.946649 | 479.836534 |
950 | 0.95 | -1,308.531523 | 7.457756 | 515.141058 |
1,000 | 1 | -1,308.959726 | 8.219196 | 567.737163 |
1,050 | 1.05 | -1,309.000147 | 8.440906 | 583.05166 |
1,100 | 1.1 | -1,308.066786 | 7.682996 | 530.699412 |
1,150 | 1.15 | -1,309.098696 | 8.505651 | 587.523883 |
1,200 | 1.2 | -1,308.997096 | 8.156313 | 563.393532 |
1,250 | 1.25 | -1,308.878993 | 7.746132 | 535.060447 |
1,300 | 1.3 | -1,308.065688 | 7.273358 | 502.403854 |
1,350 | 1.35 | -1,308.306339 | 7.531114 | 520.208224 |
1,400 | 1.4 | -1,307.960439 | 7.281711 | 502.980824 |
1,450 | 1.45 | -1,307.621232 | 7.469347 | 515.941651 |
1,500 | 1.5 | -1,307.834893 | 7.53799 | 520.683133 |
1,550 | 1.55 | -1,308.597871 | 9.001114 | 621.747784 |
1,600 | 1.6 | -1,307.993788 | 7.91002 | 546.380961 |
1,650 | 1.65 | -1,308.390354 | 8.432129 | 582.44538 |
1,700 | 1.7 | -1,308.058787 | 7.979009 | 551.146333 |
1,750 | 1.75 | -1,308.865922 | 8.815801 | 608.947363 |
1,800 | 1.8 | -1,307.760949 | 7.540937 | 520.886699 |
1,850 | 1.85 | -1,309.385204 | 9.075532 | 626.888163 |
1,900 | 1.9 | -1,309.654554 | 8.973614 | 619.848226 |
1,950 | 1.95 | -1,308.894168 | 8.518803 | 588.432373 |
2,000 | 2 | -1,308.566765 | 7.69395 | 531.456028 |
2,050 | 2.05 | -1,308.255693 | 7.73342 | 534.182429 |
2,100 | 2.1 | -1,308.891617 | 8.480145 | 585.762081 |
2,150 | 2.15 | -1,308.575661 | 8.04234 | 555.520911 |
2,200 | 2.2 | -1,309.199589 | 8.542207 | 590.049017 |
2,250 | 2.25 | -1,310.004681 | 8.761686 | 605.209366 |
2,300 | 2.3 | -1,309.242163 | 7.739435 | 534.597905 |
2,350 | 2.35 | -1,309.891163 | 8.068326 | 557.315845 |
2,400 | 2.4 | -1,309.825537 | 8.442417 | 583.156041 |
2,450 | 2.45 | -1,309.495619 | 8.200628 | 566.454585 |
2,500 | 2.5 | -1,310.031791 | 8.396281 | 579.969198 |
2,550 | 2.55 | -1,309.746336 | 8.287196 | 572.434243 |
2,600 | 2.6 | -1,309.022137 | 7.545115 | 521.175326 |
2,650 | 2.65 | -1,309.749866 | 8.07673 | 557.896398 |
2,700 | 2.7 | -1,309.353457 | 7.645892 | 528.136426 |
2,750 | 2.75 | -1,309.813669 | 8.04994 | 556.045903 |
2,800 | 2.8 | -1,309.423015 | 7.329511 | 506.282568 |
2,850 | 2.85 | -1,311.150093 | 8.763037 | 605.302747 |
2,900 | 2.9 | -1,311.028405 | 8.271644 | 571.359993 |
2,950 | 2.95 | -1,310.185019 | 7.667175 | 529.606538 |
3,000 | 3 | -1,310.148477 | 7.457614 | 515.131238 |
3,050 | 3.05 | -1,310.253715 | 8.048914 | 555.975026 |
3,100 | 3.1 | -1,309.765817 | 7.628957 | 526.966681 |
3,150 | 3.15 | -1,310.789492 | 8.301733 | 573.43839 |
3,200 | 3.2 | -1,310.654712 | 7.845307 | 541.910941 |
3,250 | 3.25 | -1,310.572134 | 7.442257 | 514.070446 |
3,300 | 3.3 | -1,310.338455 | 6.784961 | 468.668028 |
3,350 | 3.35 | -1,310.838668 | 6.990271 | 482.849742 |
3,400 | 3.4 | -1,311.056136 | 7.490467 | 517.400514 |
3,450 | 3.45 | -1,311.27987 | 7.767971 | 536.568963 |
3,500 | 3.5 | -1,311.640745 | 7.693507 | 531.425421 |
3,550 | 3.55 | -1,311.748197 | 8.411361 | 581.010867 |
3,600 | 3.6 | -1,310.765626 | 7.105272 | 490.79339 |
3,650 | 3.65 | -1,311.681128 | 8.13351 | 561.818457 |
3,700 | 3.7 | -1,310.871461 | 7.145982 | 493.605404 |
3,750 | 3.75 | -1,311.094985 | 7.088501 | 489.634944 |
3,800 | 3.8 | -1,311.48685 | 7.186068 | 496.374284 |
3,850 | 3.85 | -1,311.024644 | 7.271884 | 502.302006 |
3,900 | 3.9 | -1,311.286077 | 7.307131 | 504.736676 |
3,950 | 3.95 | -1,311.092469 | 6.677521 | 461.246666 |
4,000 | 4 | -1,311.929108 | 7.565978 | 522.616438 |
4,050 | 4.05 | -1,311.282346 | 7.228958 | 499.33691 |
4,100 | 4.1 | -1,311.514834 | 7.215558 | 498.411314 |
4,150 | 4.15 | -1,311.096471 | 7.015766 | 484.610791 |
4,200 | 4.2 | -1,311.621949 | 7.32256 | 505.802463 |
4,250 | 4.25 | -1,311.566755 | 7.72235 | 533.417738 |
4,300 | 4.3 | -1,310.865797 | 6.864617 | 474.170209 |
4,350 | 4.35 | -1,311.831396 | 7.704008 | 532.150784 |
4,400 | 4.4 | -1,311.390941 | 7.535202 | 520.490596 |
4,450 | 4.45 | -1,311.672749 | 7.799058 | 538.716325 |
4,500 | 4.5 | -1,310.818886 | 6.966691 | 481.220929 |
4,550 | 4.55 | -1,310.740411 | 7.172435 | 495.43263 |
4,600 | 4.6 | -1,310.809974 | 7.113592 | 491.368039 |
4,650 | 4.65 | -1,310.961833 | 6.878042 | 475.097538 |
4,700 | 4.7 | -1,311.635356 | 7.859912 | 542.919758 |
4,750 | 4.75 | -1,310.784981 | 7.180448 | 495.986144 |
4,800 | 4.8 | -1,311.577095 | 8.058122 | 556.611011 |
4,850 | 4.85 | -1,311.144532 | 7.833847 | 541.119314 |
4,900 | 4.9 | -1,310.841702 | 7.229248 | 499.356984 |
4,950 | 4.95 | -1,310.314156 | 7.065697 | 488.059734 |
End of preview. Expand in Data Studio
MLIP Stack Docker (CPU)
Ready-to-run Docker image bundling five machine-learning interatomic potential (MLIP) stacks in isolated conda environments. Built for CPU-only machines (no CUDA required). All environments use Python 3.11.
| Env name | Stack | Key packages |
|---|---|---|
grace |
GRACE (tensorpotential) | tensorflow |
nequip |
NequIP / Allegro | nequip, torch 2.12 (cpu) |
esen |
eSEN (fairchem) | fairchem-core, torch 2.4.1 (cpu), torch_scatter/sparse |
tace2 |
TACE | tace (git pin), torch 2.13 (cpu) |
mbd |
SevenNet and general MLIP tooling | sevenn, torch 2.8 (cpu), ase, pymatgen |
Quick start
# download and load (~7.6 GB compressed, ~28 GB on disk)
wget https://huggingface.co/datasets/Seanmonami/mlip-stack-docker/resolve/main/mlip-router-stack_2026-08-21.tar.gz
docker load < mlip-router-stack_2026-08-21.tar.gz
# interactive shell
docker run --rm -it mlip-router-stack:2026-08-21
# run something in a specific env
docker run --rm -v $PWD:/work -w /work mlip-router-stack:2026-08-21 \
micromamba run -n mbd python your_script.py
Inside the container, switch environments with micromamba run -n <env> ... or micromamba activate <env>.
Rebuilding from source
docker_build/ contains the Dockerfile and per-env requirement files (pip freezes normalized to CPU wheels). env_snapshots/ contains the original conda env export YAMLs and raw pip freezes captured from the source machine (Ubuntu 24.04, 2026-08-21).
cd docker_build && bash build.sh
Notes
- Model checkpoints are NOT included; download them via each package's own tooling (e.g.
sevenn,fairchem, GRACE model registry). - CUDA wheels were replaced with CPU wheels (
nvidia-*andtritondropped). For GPU use, rebuild with the original pins inenv_snapshots/.
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