Machine Learning for Molecular Spectroscopy

Two small projects applying machine learning to chemistry problems in Jupyter notebooks.

Project 1: Predicting Energy Levels (C2H2 and C2)

Train models to predict state energies using vibrational/rotational quantum numbers and then assess whether adding symmetry quantum numbers improves accuracy and whether the states can be clustered by symmetry.

Project 2: Predicting molecular constants for C2 and reproducing heat capacity and partition function for C2 (12 isotope)

Extract molecular constants for two mentioned states of C2 and compare against reference values; 2) Use a kernel model to reproduce heat capacity and partition function for C2 (12 isotope).