Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning Interatomic Potentials (MLIPs) have drastically improved cost for a given accuracy, their inference cost remains a bottleneck for massive systems or long timescales. To address this, we introduce a multifidelity "Mixture-of-Experts" framework based on the E(3)-equivariant Allegro architecture. Our method spatially partitions the simulation domain into a chemically complex region (e.g., reactive interfaces) and a simple region (e.g., bulk lattice), assigning models of varying capacity to each. Among the challenge...
| Authors | Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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