Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
arXiv:2607.02499v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and datasets have led to increasingly accurate and general models, the choice of optimizer for training has largely remained unexplored, defaulting to Adam and its variants in the community. Here, we implement and systematically compare a class of recently proposed matrix-structured optimizers, including Muon, SOAP, and the hybrid SOAP-Muon, for training NequIP and Allegro MLIP models. We find that these optim
The continuous drive to improve the efficiency and speed of AI model training, especially for scientific simulations, positions this research as a timely advancement in computational methods.
Faster and more label-efficient training of MLIPs can accelerate materials science, drug discovery, and chemistry by making simulations more accessible and less computationally intensive, directly impacting scientific R&D timelines.
The systematic comparison and identification of superior optimizers beyond Adam could lead to a paradigm shift in how MLIPs are trained, yielding more efficient development and deployment of these crucial AI tools.
- · Materials scientists
- · Pharmaceutical industry
- · Chemical engineers
- · Computational chemistry platforms
- · Labs without advanced computational resources
- · Companies reliant on older, slower simulation methods
Accelerated discovery of new materials and chemical compounds will occur due to more efficient MLIP training.
This could lead to a competitive advantage for nations and companies investing in AI-driven scientific research capabilities.
Reduced research cycles in materials science might enable the development of novel components critical for next-generation compute, energy, or defence technologies.
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