Machine learning has entered the field of quantum matter with applications covering quantum materials and the many-body problem. Interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science.
Among others, we use the organic materials database developed within our group as a training set for our machine learning studies. The database hosts electronic and magnetic structures of about 25,000 3-dimensional organic crystals and provides a highly complex dataset to work on. Applying machine learning we intend to provide predictions towards novel functional materials based on the properties calculated within our training sets.
“Band gap prediction for large organic crystal structures with machine learning”
Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov, Alexander V. Balatsky