Manifold learning in atomistic simulations: a conceptual review

Manifold learning in atomistic simulations: a conceptual review

Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of complex systems where even thousands of degrees of freedom are sampled....

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Bibliographic Details
Main Authors:	Jakub Rydzewski, Ming Chen, Omar Valsson
Format:	Article
Language:	English
Published:	IOP Publishing 2023-01-01
Series:	Machine Learning: Science and Technology
Subjects:	molecular dynamics dimensionality reduction manifold learning enhanced sampling
Online Access:	https://doi.org/10.1088/2632-2153/ace81a

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