Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics
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Abstract (arXiv)
High-dimensional metastable molecular system can often be characterised by a few features of the system, i.e. collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning-based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning-based approaches for finding CVs, either by computing leading eigenfunctions of infinitesimal generator or transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 52 references that are in the map. First posted on arXiv 1 Jul 2023.
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12 of 52 references.
References · 12
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- 2019Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systemscited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.