FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
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2 Pith papers cite this work. Polarity classification is still indexing.
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This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
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A Force-Kernel Reformulation of the Extended-System Adaptive Biasing Force for Free-Energy Calculations
FK-eABF replaces histogram accumulation in eABF with Gaussian force kernels and Nadaraya-Watson regression to achieve faster free-energy landscape coverage while retaining quantitative accuracy across simulation timescales.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.