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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The Lindblad equation is valid only within a finite time window controlled by the timescales of the bath, the system, and the rotating-wave approximation, with failure at early and late times.
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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?
The Lindblad equation is valid only within a finite time window controlled by the timescales of the bath, the system, and the rotating-wave approximation, with failure at early and late times.