Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics
This work provides a comparative overview for researchers in computational chemistry and molecular dynamics, but it is incremental as it synthesizes existing methods without introducing new techniques.
The paper reviews two deep learning approaches for identifying collective variables in molecular dynamics: one based on eigenfunctions of dynamical operators and another using autoencoders, and compares them numerically on illustrative examples.
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.