Explores multipole expansion in electrostatics and magnetostatics, focusing on monopole, dipole, and higher-order moments, and their mathematical representations.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
