Training a Foundation Model for Materials on a Budget
Authors: name: Teddy Koker, name: Tess Smidt

Foundation models for materials modeling are advancing quickly, but their
training remains expensive, often placing state-of-the-art methods out of reach
for many research groups. We introduce Nequix, a compact E(3)-equivariant
potential that pairs a simplified NequIP design with modern training practices,
including equivariant root-mean-square layer normalization and the Muon
optimizer, to retain accuracy while substantially reducing compute
requirements. Built in JAX, Nequix has 700K parameters and was trained in 500
A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix
ranks third overall while requiring less than one quarter of the training cost
of most other methods, and it delivers an order-of-magnitude faster inference
speed than the current top-ranked model. We release model weights and fully
reproducible codebase at https://github.com/atomicarchitects/nequix