Garden's Almanac of Matter Models

eSEN

January 2025

Checkpoint Params Polaris Sophia Perlmutter Delta Frontier Della
esen-30m-mptrj 30M
esen-30m-oam 30M
esen-30m-omat 30M
esen-md-direct-all-omol 50.7M
esen-sm-conserving-all-omol 6.3M
esen-sm-direct-all-omol 6.3M
esen:custom
verified, last 30 days installed, not recently verified not installed

No cluster has installed any checkpoint of this model yet.


Running this model
1# from a job or interactive session on a supported cluster:
2from rootstock import RootstockCalculator
3
4# this model accepts charge and spin
5atoms.info["charge"] = -1
6atoms.info["spin"] = 2
7
8with RootstockCalculator(
9 cluster=YOUR_CLUSTER_ID, # eg, "sophia", "perlmutter"
10 checkpoint="esen-30m-mptrj",
11 device="cuda",
12) as calc:
13 # model is now running in subprocess on compute node
14 atoms.calc = calc
15 atoms.get_potential_energy()

Environments

Rootstock runs each model family inside an isolated Python environment defined by a single file. This environment file includes the specific dependencies needed, plus a setup() function that loads the model and returns an ASE calculator. These files are usually almost identical for a given model family, but because of cluster-specific quirks (eg, an old CUDA driver) the dependencies and setup code can vary a bit.

esen_env.py
1# /// script
2# requires-python = ">=3.10,<3.11"
3# dependencies = [
4# "torch>=2.4.0",
5# "fairchem-core>=2.0.0",
6# "ase>=3.22",
7# "torch-geometric",
8# ]
9#
10# [tool.uv]
11# find-links = ["https://data.pyg.org/whl/torch-2.4.0+cu121.html"]
12# ///
13"""eSEN env — hosts FAIRChem eSEN single-task checkpoints.
14
15OMol checkpoints expect `charge` and `spin` in `atoms.info`.
16"""
17
18CHECKPOINTS = {
19 "esen-md-direct-all-omol": "esen-md-direct-all-omol",
20 "esen-sm-conserving-all-omol": "esen-sm-conserving-all-omol",
21 "esen-sm-direct-all-omol": "esen-sm-direct-all-omol",
22}
23
24
25def setup(checkpoint: str, device: str = "cuda"):
26 from fairchem.core import FAIRChemCalculator, pretrained_mlip
27
28 predictor = pretrained_mlip.get_predict_unit(CHECKPOINTS[checkpoint], device=device)
29 return FAIRChemCalculator(predictor)
30

Built on Polaris: 2026-05-12

Couldn't load the current environments from Rootstock.


References
  1. Fu, Xiang, Wood, Brandon M., Barroso-Luque, Luis, Levine, Daniel S., Gao, Meng, Dzamba, Misko, Zitnick, C. Lawrence, Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction, arXiv, 2025.