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

2026-07-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computational Science & Simulation · Depth: Expert, quick

Summary

A study on machine learning interatomic potentials (MLIPs) investigates the impact of optimizer choice, moving beyond the common use of Adam and its variants. Researchers implemented and systematically compared matrix-structured optimizers, including Muon, SOAP, and the hybrid SOAP-Muon, for training NequIP and Allegro MLIP models. The findings indicate that SOAP and SOAP-Muon significantly outperform Adam in both convergence speed and final accuracy. While Muon offered only partial improvements, SOAP and SOAP-Muon proved to be robust and consistently strong methods. These performance gains were particularly notable when training with partial force supervision. The research highlights optimizer selection as a crucial yet often overlooked design factor for enhancing MLIP performance.

Key takeaway

For research scientists developing or training machine learning interatomic potentials (MLIPs), you should critically re-evaluate your optimizer choice. Moving beyond default Adam, consider implementing and benchmarking matrix-structured optimizers like SOAP or SOAP-Muon. These can significantly accelerate convergence and improve final accuracy, particularly when working with partial force supervision. Integrating these advanced optimizers into your workflow could yield substantial performance gains for your MLIP models.

Key insights

Matrix-structured optimizers like SOAP and SOAP-Muon significantly enhance MLIP training speed and accuracy over Adam, especially with partial force supervision.

Principles

• Optimizer choice critically impacts MLIP training.
• Matrix-structured optimizers can surpass Adam's performance.
• Partial force supervision benefits from advanced optimizers.

Method

The study systematically compared Muon, SOAP, and hybrid SOAP-Muon optimizers against Adam for training NequIP and Allegro MLIP models, evaluating convergence speed and final accuracy under full and partial force supervision.

In practice

• Consider SOAP or SOAP-Muon for MLIP training.
• Evaluate advanced optimizers for partial force supervision.
• Benchmark optimizers beyond Adam for new MLIPs.

Topics

• Machine Learning Interatomic Potentials
• Optimizer Algorithms
• SOAP Optimizer
• Muon Optimizer
• NequIP
• Allegro
• Partial Force Supervision

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.