Mixture of experts architectures for machine learning interatomic potentials
Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we systematically investigate Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures within the DPA3 framework for MLIPs and analyze the effects of routing strategies and expert designs. We show that sparse activation combined with shared experts yields substantial performance gains, and that nonlinear MoE formulations outperform MoLE when shared experts are present, underscoring the importance of nonlinear expert specialization. Furthermore, element-wise routing consistently surpasses configur...
| Authors | Yuzhi Liu, Duo Zhang, Anyang Peng et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 9 March 2026 |
| Last updated at source | 17 August 2026 |
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