Skip to content

MLFF dependency groups #1455

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
esoteric-ephemera merged 2 commits into from
Apr 6, 2026
Merged

MLFF dependency groups #1455

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6aea18b
ff dep groups
esoteric-ephemera Apr 6, 2026
8720ba5
narrow ff dep groups
esoteric-ephemera Apr 6, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 5 additions & 4 deletions docs/user/codes/forcefields.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,13 @@
# Machine Learning forcefields / interatomic potentials

`atomate2` includes an interface to a few common machine learning interatomic potentials (MLIPs), also known variously as machine learning forcefields (MLFFs), or foundation potentials (FPs) for universal variants.
These can be installed using `pip install 'atomate2[ase]'`.

***As of `atomate2==0.1.1`, most forcefield packages are opt-in only. You must install those forcefields which you plan to use.***
Running `pip install 'atomate2[forcefields]'` will install the `chgnet` package to permit you to try the forcefield classes.
You can then select other forcefields you want to use.
***As of `atomate2==0.1.2`, all forcefield packages are opt-in only. You must install those forcefields which you plan to use.***

Running `pip install 'atomate2[forcefields-demo]'` will install the `chgnet` package to permit you to try the forcefield jobs/workflows.
You can then install additional forcefield libraries.

We have made this choice both to avoid the appearance of favoritism (both `chgnet` and `atomate2` are Materials Project-supported projects), and to avoid dependency conflicts between MLFF packages.
If you need a sense of which forcefields are compatible, you can use the [pyproject.toml](https://github.com/materialsproject/atomate2/blob/a8bc6505e439503a114f5346aec916aafae7f27b/pyproject.toml#L90) to see which versions are grouped together for testing.

Most of `Maker` classes using the forcefields inherit from `atomate2.forcefields.utils.ForceFieldMixin` to specify which forcefield to use.
Expand Down
10 changes: 4 additions & 6 deletions pyproject.toml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -55,17 +55,15 @@ defects = [
"python-ulid>=2.7",
]

forcefields = [
"ase>=3.26.0",
"chgnet>=0.4.2",
]
ase = ["ase>=3.26.0"]
ase-ext = ["tblite>=0.3.0; platform_system=='Linux'"]
forcefields-demo = ["chgnet>=0.3.8","atomate2[ase]"]

torchsim = [
"torch-sim-atomistic==0.5.0; python_version >= '3.12'"
]
jdftx = ["pymatgen==2026.3.23"]
approxneb = ["pymatgen-analysis-diffusion>=2024.7.15"]
ase = ["ase>=3.26.0"]
ase-ext = ["tblite>=0.3.0; platform_system=='Linux'"]
openmm = [
"mdanalysis>=2.8.0",
"openmm-mdanalysis-reporter>=0.1.0",
Expand Down
Loading