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 range of interactions. While machine learning force fields (MLFFs) offer near-quantum accuracy, the ubiquitous message-passing layers miss long-range many-body effects. Here we introduce the Multiscale Structural Ensemble (MuSE), a hierarchical model that uses Soft Coarse-Graining Pooling to construct coarse representa
The increasing complexity of scientific machine learning models necessitates new architectures to handle multiscale interactions effectively, addressing current limitations in accuracy and scope.
This development pushes the boundaries of scientific machine learning, enabling more accurate and comprehensive simulations of complex physical and chemical systems, with broad implications for R&D.
The introduction of the Multiscale Structural Ensemble (MuSE) potentially allows ML models to capture long-range many-body effects that were previously missed by existing machine learning force fields.
- · Materials science researchers
- · Drug discovery companies
- · Chemical engineering sectors
- · AI model developers
- · Traditional simulation software vendors
- · Research groups reliant on narrow-scope ML models
More precise and efficient computational design of new materials and molecules becomes possible.
Reduced experimental trial-and-error in R&D, accelerating innovation across various industries.
The development of entirely new classes of materials or therapies previously inaccessible due to computational limitations.
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Read at arXiv cs.LG