.. _tutorial_omnip1:

OMNI-P1
========

OMNI-P1 is the all-in-one universal potential that was simultaneously trained on two levels of theory, DFT (wB97X/def2-TZVPP) and coupled cluster (CCSD(T)*/CBS), see:

Details are in the paper:

- Yuxinxin Chen, `Pavlo O. Dral <http://dr-dral.com>`__. `One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels <https://doi.org/10.1021/acs.jctc.5c00858>`__. *J. Chem. Theory Comput.*,  **2025**, *21*, 8762–8772. DOI: 10.1021/acs.jctc.5c00858. Preprint on ChemRxiv: https://doi.org/10.26434/chemrxiv-2024-ng3ws (2024.09.18) | arXiv: https://arxiv.org/abs/2409.12015.

The procedure is general and can be applied for simultaneous training on more levels of theory.

Due to the uniqueness of this model, you can choose the target level of theory to make predictions at. Default is CCSD(T)*/CBS as a better level. After you choose the level, you can use the model for the typical simulations such as :ref:`geometry optimizations <tutorial-geomopt>` or :ref:`molecular dynamics <tutorial_md>`.

Below is a simple example for geometry optimizations:

.. code-block:: python
    
    initmol = ml.molecule.from_xyz_file('init.xyz')
    omnip1 = ml.methods(method='omnip1', level='cc')
    geomopt = ml.optimize_geometry(model=omnip1, initial_molecule=initmol)
    final_mol = geomopt.optimized_molecule
    print('Optimized coordinates:')
    print(final_mol.get_xyz_string())
    final_mol.write_file_with_xyz_coordinates(filename='final.xyz')