.. _example-ml-tpa:

Two-photon absorption cross sections with machine learning
==========================================================

Two-photon absorption (TPA) is an important physical phenomenon which can be exploited in many different applications like upconverted laser. MLatom implements machine learning method predicting TPA cross section for a new molecule just by providing its SMILES (see `this paper <http://doi.org/10.1002/advs.202204902>`_ in Adv. Sci. for details).

Input files
-----------

Here we show how to calculate TPA cross section for RHODAMINE 6G and RHODAMINE 123 molecules with MLatom input file mltpa.inp:

.. code:: text

    MLTPA
    SMILESfile=Smiles.csv
    auxfile=_aux.txt

This input requires ``Smiles.csv`` file with SMILES of molecules:

.. code:: text

    CCNC1=CC2=C(C=C1C)C(=C3C=C(C(=[NH+]CC)C=C3O2)C)C4=CC=CC=C4C(=O)OCC.[Cl-]
    COC(=O)C1=CC=CC=C1C2=C3C=CC(=N)C=C3OC4=C2C=CC(=C4)N.Cl

and optional ``_aux.txt``, which defines the wavelength_lowbound, wavelength_upbound, and Et30 for making predicitons:

.. code:: text

    600,850,55.4
    600,600,33.9

After you prepared your input files mltpa.inp, :download:`Smiles.csv`, and :download:`_aux.txt`, you can run MLatom as usual.

Computational results
---------------------

After the calculations finish, the predicted TPA cross section values are saved in two files for two molecules: ``tpa1.txt`` and ``tpa2.txt``. For our examples, they look like:

.. parsed-literal::

    wavelength,predicted_sigma (GM)
    600.0,285.19455
    610.0,297.71707
    620.0,284.11694
    ......
    810.0,121.51988
    820.0,116.537994
    830.0,118.04909
    840.0,103.65925
    850.0,113.72374

and

.. parsed-literal::
    
    wavelength,predicted_sigma (GM)
    600.0,138.2346