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Author:
Yuan Chiang
Summary:
Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond error-based regression metrics
Latest version:
0.2.0
Required dependencies:
ase
|
dask
|
dask-jobqueue
|
datasets
|
huggingface-hub
|
loguru
|
prefect
|
pymatgen
|
safetensors
|
tables
|
torch
Optional dependencies:
alignn
|
chgnet
|
deepmd-kit
|
dgl
|
e3nn
|
fairchem-core
|
ipykernel
|
ipywidgets
|
mace-torch
|
matgl
|
mattersim
|
mdanalysis
|
nequip
|
orb-models
|
plotly
|
pre-commit
|
pymatgen
|
pytest
|
pytest-xdist
|
quests
|
ruff
|
sevenn
|
streamlit
|
torch
|
torch-dftd
|
torchani
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