arXiv:2606.09480v1 Announce Type: new Abstract: Molecular systems involve interactions across multiple spatial scales, from local coordination and short-range perturbations to long-range electrostatic and solvent-mediated effects. However, most molecular representation learning methods rely on manually predefined scales, and the task-optimal modeling scale may not coincide with these fixed levels. This study introduces a loss-guided adaptive scale refinement framework for molecular force prediction, treating predefined scales as initial anchors and discovering task-effective resolutions throug
The paper addresses a fundamental limitation in molecular simulation — the reliance on fixed scales — leveraging advancements in AI to create more adaptive and accurate models.
Improved molecular force prediction through adaptive scaling could significantly accelerate drug discovery, materials science, and synthetic biology, reducing R&D cycles and costs.
Molecular representation learning shifts from solely relying on predefined scales to dynamically discovering task-effective resolutions, leading to more accurate and efficient simulations.
- · Pharmaceutical R&D
- · Chemical engineering
- · Materials science
- · AI for science platforms
- · Traditional molecular simulation software reliant on fixed-scale models
More accurate and faster molecular simulations will lead to quicker identification of promising drug candidates or novel materials.
Reduced R&D timelines could accelerate market entry for new therapies and advanced materials, fostering innovation in related sectors.
The ability to simulate complex molecular interactions with greater precision could unlock entirely new fields of bioengineering and material design, potentially leading to breakthroughs in energy storage or disease prevention.
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Read at arXiv cs.LG