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About this paper
Timothée Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil
The authors develop a generator-based method to learn slow molecular transitions from biased simulations, using the known bias and diffusion to correct a local description of the dynamics rather than reweighting a transfer operator. The method learns generator eigenfunctions and eigenvalues. On one alanine-dipeptide dataset, its first eigenfunction separated metastable states despite only two transitions; this is not a guarantee.
Summary by alphaXiv.
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