Get started with MLatom\@XACS
===============================================================

This tutorial will use the `MLatom\@XACS program <http://MLatom.com>`__.

- :ref:`Slides <get_started_mlatom_slides>`
- :ref:`Video <get_started_mlatom_video>`
- :ref:`Geometry optimization <get_started_mlatom_geomopt>`

.. _get_started_mlatom_slides:

Slides
------

:download:`Slides <_static/mlatom/XACSW2024_20240702_Dral_wm.pdf>`:

.. raw:: html
    
    <embed src="_static/mlatom/XACSW2024_20240702_Dral_wm.pdf" width="741" height="310" aligh="left" type="application/pdf">

.. _get_started_mlatom_video:

Video
-----

:red:`Coming soon.`

.. raw:: html

    <video width="640" height="380" controls>
        <source src="_static/mlatom/XACSW2024_20240702_Dral.mp4" type="video/mp4">
    </video>

.. _get_started_mlatom_geomopt:

Geometry optimization
---------------------

We will get started by showcasing the power of ML to supercharge the computational chemistry simulations. We will use the most recent and advanced concept of performing simulations with the universal and updatable AI-enhanced quantum mechanical (UAIQM) models:

.. raw:: html

   <iframe src="https://player.bilibili.com/player.html?isOutside=true&aid=1205972334&bvid=BV1Zf421B7GB&cid=1597373556&p=1&autoplay=0" scrolling="no" border="0" frameborder="no" framespacing="0" allowfullscreen="true" height=500 width=800></iframe>

Let's say you want to optimize the molecular geometry. Now you do not need to worry which method (DFT or *ab initio*, etc.) to choose: MLatom will choose the best model automatically for you.

.. admonition:: Example 1.

    The input is simple:

    .. code-block:: 

        UAIQM
        geomopt
        xyzfile='5
        
        C         0.0000000000        0.0000000000        0.0000000000
        H         1.0870000000        0.0000000000        0.0000000000
        H        -0.3623333220       -1.0248334322       -0.0000000000
        H        -0.3623333220        0.5124167161       -0.8875317869
        H        -0.3623333220        0.5124167161        0.8875317869
        ' 

In the output file, you will get the information which UAIQM model was selected:

.. code-block::

    Selected UAIQM method: uaiqm_gfn2xtbstar@cc
    Selected version: 20240106

and you can check the model's composition `online <https://xacs.xmu.edu.cn/docs/mlatom/tutorial_uaiqm.html#uaiqm-library>`__.

If your molecule happened to be neutral and closed shell and only contain the CHNO elements, UAIQM will give you the CCSD(T)/CBS-level result but at a very fast speed (usually even faster than DFT).

The MLatom output ``.log`` file will contain information about the optimization iterations and print out the final optimized geometry, which you can also find in the ``optgeoms.xyz``.

Note that same kind of input works for other, QM and ML, methods, e.g. for DFT:

.. admonition:: Example 2.

    .. code-block:: 

        # example for DFT:
        B3LYP/6-31G*
        # example for ab initio:
        # CCSD(T)/cc-pVDZ
        # example for very fast universal ML model (also approaching CCSD(T)/CBS):
        # ANI-1ccx
        geomopt
        xyzfile='5
        
        C         0.0000000000        0.0000000000        0.0000000000
        H         1.0870000000        0.0000000000        0.0000000000
        H        -0.3623333220       -1.0248334322       -0.0000000000
        H        -0.3623333220        0.5124167161       -0.8875317869
        H        -0.3623333220        0.5124167161        0.8875317869
        ' 

You can play with these different options.

Since MLatom is a data-drive package, the optimization trajectory is also saved in the json format, i.e., you will find files like ``opttraj.json`` (it is also available in XYZ format for convenience: ``opttraj.xyz``).

For more options on how to use MLatom for geometry optimizations, you can check out the `dedicated tutorial <https://xacs.xmu.edu.cn/docs/mlatom/tutorial_geomopt.html>`__. For example, you can use MLatom for the `optimization with constraints <https://xacs.xmu.edu.cn/docs/mlatom/tutorial_geomopt.html#optimization-with-constraints>`__ too.

The power of MLatom is that you can use different ML models to do many types of simulations, including geometry optimization as we will see in the :ref:`basics of ML <ml-basics>`.