AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential

TL;DR

AlphaNet is a novel equivariant neural network model that significantly enhances the accuracy and scalability of interatomic potential predictions for molecular and materials simulations.

Contribution

It introduces a local-frame-based equivariant architecture with learnable geometric transitions, improving predictive precision and computational efficiency over existing models.

Findings

Achieves state-of-the-art energy and force prediction accuracy.

Demonstrates superior performance on large-scale molecular and materials datasets.

Ensures scalability and transferability across diverse atomic systems.

Abstract

Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials.