arXiv:2605.21722v1 Announce Type: cross Abstract: Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS suffer from mode collapse and fail to sample high-energy barrier regions between modes, which is critical for free energy estimation and understanding phase transitions. We propose Metadynamics Discrete Neural Sampler (MetaDNS), a general framework integrating well-tempered metadynamics into discrete diffusion or autoregressive samplers. By maintaining an adaptive, h
The continuous development in discrete neural samplers and the persistent challenge of mode collapse in AI models drive the need for more robust exploration techniques.
This development allows AI models to better explore complex, high-energy barrier regions, critical for advancing free energy estimation in materials science and understanding phase transitions, which has broad implications for drug discovery, battery design, and fundamental physics research.
The MetaDNS framework improves the ability of discrete neural samplers to avoid mode collapse, leading to more accurate and complete sampling of discrete distributions. This means more reliable simulations and predictions in machine learning and computational physics.
- · AI researchers
- · Materials scientists
- · Drug discovery sector
- · Computational physicists
- · Developers of less robust discrete samplers
- · Companies relying on less efficient simulation methods
Improved accuracy in AI-driven material simulations and molecular dynamics.
Accelerated discovery of new materials with desired properties and more efficient drug candidates.
Potential for a paradigm shift in how complex physical and chemical systems are modeled, impacting energy storage and medical technologies.
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Read at arXiv cs.LG