No Pretraining, No Equivariant Architecture – Learning MLIPs without Explicit Equivariance (opens in new tab)

Machine-learned interatomic potentials (MLIPs) have become a cornerstone of modern computational chemistry, enabling simulations that approach quantum accuracy at a fraction of the cost of traditional methods such as density functional theory (DFT). However, a central challenge in designing MLIPs lies in respecting the fundamental symmetries of molecular systems, especially rotational and translational invariance, while maintaining scalability and flexibility.