Explore a comprehensive lecture on E(3)-Equivariant Interatomic Potentials theory and applications presented by Ilyes Batatia from the University of Cambridge. Delve into the world of interatomic potentials, symmetries, and machine learning approaches in quantum mechanics. Discover the unified understanding of ML potentials, including Message Passing Neural Networks, body-ordered messages, and the Generalized Atomic Cluster Expansion (ACE). Examine the Multi-ACE framework, MPNN-ACE identification, and the classification of models within this unified design space. Investigate the BOTNet as a body-ordered Equivariant MPNN and explore the influence of non-linearities and data normalization. Address ML Interatomic Potentials limitations and learn how many-body messages can solve MPNN limitations. Gain insights into data efficiency, extrapolation capabilities, and practical applications such as H transfer in acetyl-acetone. Conclude with an outlook on future developments in this field.