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We’re pleased to announce Sage 2.3.0 Release Candidate 2 (rc2)! Sage 2.3.0 will be the first OpenFF force field to use the AshGC neural network charge model, which was trained to AM1BCC ELF10 charges, and allows for rapid charge assignment for molecules with hundreds or thousands of heavy atoms.

Please try it out and let us know if you find any substantial improvements, regressions, or other issues in comparison to Sage 2.2.1. If all goes well, we plan to make the full release in a few weeks! See the thread for an example:

Because AshGC assigns charges so quickly, use cases like quick conformer minimization are now practical. The following python code will minimize 50 different molecules from an input SDF, saving each minimized structure to an SDF file, in about 1 minute:

``` from openff.toolkit import Molecule, ForceField from tqdm import tqdm # for progress bars mols = Molecule.from_file("inputs.sdf") # loads 50 different molecules ff = ForceField("openff_unconstrained-2.3.0-rc2.offxml") ...

... for idx, mol in enumerate(tqdm(mols, desc="Processing molecules")): interchange = ff.create_interchange(mol.to_topology()) interchange.minimize() mol.conformers[0] = interchange.positions mol.to_file(f"minimized_mol_{idx}.sdf", file_format="sdf") ```

Two notes on the example: 1. The first time NAGL is used in a new environment, there may be a few seconds of extra runtime due to pytorch initialization.

The easiest way to try Sage 2.3.0 rc2 out is by making a new environment: ``` mamba create -n openff -c conda-forge "openff-toolkit>=0.17" mamba activate openff ```

Or you can update an existing environment: ``` mamba install -c conda-forge "openff-toolkit>=0.17" "openff-interchange>=0.4.6" "openff-nagl>=0.5.3" "openff-nagl-models>=2025.09" "openff-forcefields>=2025.10" ```