Learning materials properties and dynamics with graph neural network models

Machine learning emerged as an important tool in accelerating materials science research. Graph neural networks, in particular, are widely used for modeling molecules and materials as they are capable of learning representations from atomistic structures. I will give a brief introduction to graph neural network models and discuss applications for learning materials properties and dynamics. Future prospects and challenges for modeling materials with graph neural networks will also be discussed.