Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917445082546176 |
|---|---|
| author | Nascimento, Gabriel de Miranda Descoteaux, Marc L. Zichi, Laura Tan, Chuin Wei Witt, William C. Molinari, Nicola Mantha, Sriteja Kitchaev, Daniil Kornbluth, Mordechai Gadelrab, Karim Tuffile, Charles Kozinsky, Boris |
| author_facet | Nascimento, Gabriel de Miranda Descoteaux, Marc L. Zichi, Laura Tan, Chuin Wei Witt, William C. Molinari, Nicola Mantha, Sriteja Kitchaev, Daniil Kornbluth, Mordechai Gadelrab, Karim Tuffile, Charles Kozinsky, Boris |
| contents | 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 challenges in such static domain decomposition, the mechanical mismatch between models at the interface is particularly critical, as it can generate artificial stress fields and instability. We address this challenge with a co-training strategy in which the loss function includes agreement constraints -- penalties on per-atom energy and force discrepancies between models evaluated on shared bulk environments -- forcing the independent models to learn a consistent physical description of the bulk material. We validate this approach on a realistic Pt+CO catalytic system, demonstrating that the co-trained models maintain exact energy conservation, align their bulk mechanical response (e.g., equation of state and bulk modulus), and achieve predictive accuracy comparable to a full high-fidelity simulation at more than twice the computational speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26143 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Nascimento, Gabriel de Miranda Descoteaux, Marc L. Zichi, Laura Tan, Chuin Wei Witt, William C. Molinari, Nicola Mantha, Sriteja Kitchaev, Daniil Kornbluth, Mordechai Gadelrab, Karim Tuffile, Charles Kozinsky, Boris Computational Physics Materials Science Machine Learning 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 challenges in such static domain decomposition, the mechanical mismatch between models at the interface is particularly critical, as it can generate artificial stress fields and instability. We address this challenge with a co-training strategy in which the loss function includes agreement constraints -- penalties on per-atom energy and force discrepancies between models evaluated on shared bulk environments -- forcing the independent models to learn a consistent physical description of the bulk material. We validate this approach on a realistic Pt+CO catalytic system, demonstrating that the co-trained models maintain exact energy conservation, align their bulk mechanical response (e.g., equation of state and bulk modulus), and achieve predictive accuracy comparable to a full high-fidelity simulation at more than twice the computational speed. |
| title | Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations |
| topic | Computational Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2604.26143 |