diff --git a/docs/user/codes/forcefields.md b/docs/user/codes/forcefields.md
index 503b4cd76b..5b70893e76 100644
--- a/docs/user/codes/forcefields.md
+++ b/docs/user/codes/forcefields.md
@@ -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.
diff --git a/pyproject.toml b/pyproject.toml
index fd9090fdbf..e3ea24ab53 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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",