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And a more low function called `gfnff` can be found in [the code](https://github.com/LiuGaoyong/PyGFNFF/blob/main/pygfnff/_pygfnff.py).\n\n```python\nfrom ase import Atoms\nfrom ase.build import molecule\nfrom ase.optimize import BFGS\nfrom scipy.spatial.distance import pdist\n\nfrom pygfnff import GFNFF\n\natoms = Atoms(molecule(\"CO\"), calculator=GFNFF())\nopt = BFGS(atoms, logfile=\"-\", trajectory=None)\nopt.run(fmax=0.03, steps=50)\ne = atoms.get_potential_energy()\nprint(f\"Energy: {atoms.get_potential_energy():.3f}eV\")\nprint(f\"C=O Length: {pdist(atoms.positions).item():.3f}\\u212b\")\n\n# Output:\n#       Step     Time          Energy          fmax\n# BFGS:    0 19:56:53       -9.188347        2.478131\n# BFGS:    1 19:56:53       -9.050233        7.166941\n# BFGS:    2 19:56:53       -9.215231        0.433918\n# BFGS:    3 19:56:53       -9.215990        0.070653\n# BFGS:    4 19:56:53       -9.216011        0.000889\n# Energy: -9.216eV\n# C=O Length: 1.129Å\n```\n\n\n\n### Reference\n\n1. S.Spicher, S.Grimme. Robust Atomistic Modeling of Materials, Organometallic, and Biochemical Systems (2020), DOI: https://doi.org/10.1002/anie.202004239\n2. A standalone library of the GFN-FF method. https://github.com/pprcht/gfnff/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliugaoyong%2Fpygfnff","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fliugaoyong%2Fpygfnff","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliugaoyong%2Fpygfnff/lists"}