Equivariant Interatomic Potentials without Tensor Products

TL;DR

Geodite is a new equivariant message-passing architecture for interatomic potentials that replaces tensor products, achieving high accuracy and 3-5x faster computation, enabling efficient large-scale atomistic simulations.

Contribution

The paper introduces Geodite, an innovative equivariant architecture that replaces tensor products, balancing accuracy and computational efficiency for atomistic modeling.

Findings

Achieves accuracy comparable to leading methods on materials benchmarks.

Runs 3-5 times faster than similar models with high accuracy.

Enables faster large-scale atomistic simulations and high-throughput screening.

Abstract

Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories dataset of inorganic crystals, Geodite-MP achieves accuracy competitive with leading methods on benchmarks for materials stability prediction, thermal conductivity, phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive.