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New optimizers outperform Adam for faster MLIP training · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

A new research paper explores the impact of optimizers on the training of machine learning interatomic potentials (MLIPs), a key AI application in scientific simulation. The study found that matrix-structured optimizers like SOAP and Muon can significantly outperform the commonly used Adam optimizer in terms of convergence speed and final accuracy. These improvements are especially notable when using partial force supervision, suggesting that optimizer selection is a critical, yet often overlooked, factor in developing effective MLIPs. AI

IMPACT Introduces novel optimizers that could accelerate AI-driven scientific simulations and improve model accuracy.

RANK_REASON The cluster contains an academic paper detailing new methods for training machine learning models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New optimizers outperform Adam for faster MLIP training · 3 sources tracked

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The cluster contains an academic paper detailing new methods for training machine learning models.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng, Laura Zichi, Chuin Wei Tan, Marc L. Descoteaux, Boris Kozinsky ·

    Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    arXiv:2607.02499v1 Announce Type: cross 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 …

  2. arXiv cs.AI TIER_1 English(EN) · Boris Kozinsky ·

    Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    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, defa…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    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, defa…

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