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New optimizers outperform Adam for faster MLIP training

By PulseAugur Editorial · [1 sources] ·

Researchers have explored 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 new optimizers showed particular promise when used with partial force supervision, suggesting that the choice of optimizer is a critical, yet often overlooked, factor in developing effective MLIPs. AI

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

RANK_REASON Research paper detailing new methods for training machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New optimizers outperform Adam for faster MLIP training

COVERAGE [1]

  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 …

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