Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.
Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.
