SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint
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A review that contrasts common assumptions about the Lindblad equation with refined expectations drawn from examples, culminating in a checklist for assessing its breakdown.
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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
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Is Lindblad for me?
A review that contrasts common assumptions about the Lindblad equation with refined expectations drawn from examples, culminating in a checklist for assessing its breakdown.