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 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:

MLTPA
SMILESfile=Smiles.csv
auxfile=_aux.txt

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

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:

600,850,55.4
600,600,33.9

After you prepared your input files mltpa.inp, Smiles.csv, and _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:

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

wavelength,predicted_sigma (GM)
600.0,138.2346