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Found 3 papers, 1 papers with code
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations as the geometry and density of the learned manifold are biased.
Learning Stochastic Representations of Physical Systems
Learning representations of physical systems is an important problem at the interface of statistical physics and machine learning.
Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling
We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution.
Chemical Physics Computational Physics
