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New MuSE Model Accurately Captures Multiscale Interactions in Physics

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced the Multiscale Structural Ensemble (MuSE), a novel hierarchical model designed to address the challenge of predicting emergent interactions across multiple scales in physical systems. Unlike existing scientific ML models that often focus on narrow interaction ranges, MuSE employs Soft Coarse-Graining Pooling to create coarse representations, enabling MLFF modules to operate effectively across different scales. This architecture-agnostic model has been demonstrated to accurately capture quantum-mechanical interactions in various applications, including biomolecule folding and molecule-graphene nanostructures, outperforming other recent long-range ML models. AI

RANK_REASON The cluster contains an academic paper detailing a new machine learning model for scientific applications.

Read on arXiv cs.LG →

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New MuSE Model Accurately Captures Multiscale Interactions in Physics

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · \`Alex Sol\'e, Sergio Su\'arez-Dou, Albert Mosella-Montoro, Silvia G\'omez-Coca, Eliseo Ruiz, Alexandre Tkatchenko, Javier Ruiz-Hidalgo ·

    Machine Learning Multiscale Interactions

    arXiv:2605.25710v1 Announce Type: cross Abstract: Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on a narrow …

  2. arXiv cs.LG TIER_1 English(EN) · Javier Ruiz-Hidalgo ·

    Machine Learning Multiscale Interactions

    Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on a narrow range of interactions. While machine learning forc…

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