Machine learning techniques in atomic, molecular, and optical physics

Artificial intellegence may help to ellucidate intrincate chemical processes and complex electronic structure problems

An open database for spectroscopic constants of diatomic molecules

Society evolves on the road to the information dominated era, where information is available to the user with just a click of the mouse or by touching a screen. Technology advances according to the social demands, therefore novel techniques to deal with information are being developed. In our group, we use these techniques to prompt the evolution of the molecular physics community towards the technological paradigm.

We are developing an open database for spectroscopic constants of diatomic molecules. The purpose of this project is to deploy a user-friendly website linked with a dynamic database, from which every user can retrieve the spectroscopy constants of a given molecule. The user can also upload new spectroscopic constants. Therefore, we expect to have a complete and open-access database for diatomic molecules. The database will help researchers from ultracold physics, spectroscopy, and astrophysics.

Machine Learning techniques for predicting laser cooling candidates

The words machine learning is part of the lexicon of the information age. Surprisingly enough, most of the telematic tools that we use every day are based on machine learning techniques, such as social media or web browsers. So, what is machine learning? Machine learning is a set of algorithms capable of finding correlations between data points of a given data set (training set). This learned pattern is used to predict unknown outputs from a given input (test set).

The degree of control developed in the area of atomic, molecular, and optical physics has motivated many applications of atoms and molecules in different fields of physics and chemistry. In particular, atomic and molecular systems are relevant for quantum information, ultracold chemistry, and search of physics beyond the standard model. In our group, we exploit the capabilities of machine learning techniques to find correlations between different properties of molecules. Therefore, having the possibility of predicting which molecules are the most suitable for a given application.


Xiangyue Liu, Gerard Meijer and Jesús Pérez-Ríos
On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach
RSC Advances 11, 14552
Miruna T. Cretu and Jesús Pérez-Ríos
Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression
PCCP 23, 2891
X. Liu, G. Meijer and Jesús Pérez-Ríos
A data-driven approach to determine dipole moments of diatomic molecules
Phys. Chem. Chem. Phys., 22, 24191-24200
X. Liu, S. Truppe, G. Meijer and Jesús Pérez-Ríos
The diatomic molecular spectroscopy database
Cheminform 12, 31
I. C. Stevenson and Jesús Pérez-Ríos
Genetic based fitting techniques for high precision potential energy curves of diatomic molecules
Journal of Physics B: Atomic, Molecular and Optical Physics 52, 105002
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