Learning Together: Training Interatomic Potentials to Multiple Datasets

The development of machine learning interatomic potentials has led to an abundance of datasets containing quantum mechanical calculations for molecular and material systems. However, using the information from different datasets together remains a challenge due to the varying levels of theory employed. In this talk, we show that techniques can be used to fit an interatomic potential to multiple organic molecule datasets and that this yields ML potentials with improved accuracies for a variety of tasks.