README.md

metadata

title: AceFF-2 ML Potential Explorer
emoji: ⚛️
colorFrom: purple
colorTo: green
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
short_description: Energy, forces & relaxation with the AceFF-2 ML potential
python_version: '3.12'
startup_duration_timeout: 30m
license: apache-2.0

AceFF-2 — Machine Learning Potential for Small Molecules

Interactive demo for AceFF ("AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules", Acellera), a neural interatomic potential for drug-like molecules built on the TensorNet v2 architecture in TorchMD-Net.

What it does

• Input a small molecule as an XYZ block, PDB, or SMILES (SMILES gets a 3D geometry embedded with RDKit).
• Set the net charge (−2…+2) and pick a checkpoint: AceFF-2.0 or AceFF-2-OMOL (adds Na, K).
• Single point: potential energy (eV and kcal/mol) and per-atom forces (eV/Å), visualized as force arrows on an interactive 3Dmol.js viewer.
• Relax: optionally minimize the geometry with ASE's LBFGS at the AceFF-2 potential and download the relaxed XYZ.

Supported elements: H, B, C, N, O, F, Si, P, S, Cl, Br, I (AceFF-2.0) or with Na, K (OMOL). Small molecules only. Model and code are Apache-2.0 / MIT licensed. Example molecules are taken from the Acellera usage docs (caffeine, aspirin, acetate) — geometries embedded locally with RDKit.