Machine learning interatomic potentials (ML-IPs) have now established themselves as a key technique in atomistic modeling. They allow the simulation of many diverse types of systems, from the molecular to the solid state, at the accuracy of highly sophisticated electronic structure methods but at a greatly reduced cost. While the general methodology of training and validating a machine learning potential has been well established, many codes and integrated software applications exist to perform these tasks. Since many of these come with a high entry barrier, there is still a need to educate young and early-career researchers in these tasks, as well as provide a pathway to enter the field and make valuable contributions for researchers who have promising ideas that could benefit from the application of ML-IPs.
