Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models for quantum-accurate physical simulations. However, uMLIP training requires second-order derivatives, which lack corresponding parallel training frameworks; moreover, scaling to the billion-parameter regime causes explosive growth in computation and communication overhead, making its training a tremendous challenge. We introduce MatRIS-MoE, a billion-parameter Mixture-of-Experts model built upon invariant architecture, and {Janus}, a pioneering high-dimensional distributed...
| Authors | Yuanchang Zhou, Hongyu Wang, Yiming Du et al. |
|---|---|
| Primary category | cs.DC |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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