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 a mouse click or by touching a screen. Technology advances according to 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 toward the technological paradigm.
We have developed an open database for spectroscopic constants of diatomic molecules. This project aims 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 a complete and open-access database for diatomic molecules. The database will help researchers from ultracold physics, spectroscopy, and astrophysics.
Our group believes that scientific progress is open to everyone willing to learn, ask questions, and get perplexed by the beauty of Nature. Our enterprise aims to contribute to scientific progress in molecular spectroscopy's small but significant field. Do you want to contribute to improving molecular physics and chemical physics? If your answer is yes, then please click here.
Machine Learning for predicting the best molecules for a given application
The words machine learning is part of the lexicon of the information age. Surprisingly enough, most of the telematic tools we use daily 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 that finds correlations between a given data set (training set). Then, this learned pattern is used to predict unknown outputs from a given input (test set).
The degree of control developed in 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 physics search beyond the standard model. In our group, we
exploit the capabilities of machine learning techniques to find correlations between
different atomic and molecular properties, like the second virial coefficient or the dipole moment. Therefore, having the possibility of predicting which molecules are the most suitable
for a given application.