Using data-reduction techniques to analyse biomolecular trajectories
arXiv:1907.04170
Abstract
This chapter discusses the way in which dimensionality reduction algorithms such as diffusion maps and sketch-map can be used to analyze molecular dynamics trajectories. The first part discusses how these various algorithms function, as well as practical issues such as landmark selection and how these algorithms can be used when the data to be analyzed, comes from enhanced sampling trajectories. In the later parts, a comparison between the results obtained by applying various algorithms to two sets of sample data is performed and discussed. This section is then followed by a summary of how one algorithm, in particular, sketch-map, has been applied to a range of problems. The chapter concludes with a discussion on the directions that we believe this field is currently moving.
References in corpus (9)
- Escaping free-energy minima
- Comparing molecules and solids across structural and alchemical space
- From Metadynamics to Dynamics
- The double-funnel energy landscape of the 38-atom Lennard-Jones cluster
- Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems
- Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
- A self-learning algorithm for biased molecular dynamics
- Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond
- Recognizing Local and Global Structural Motifs at the Atomic Scale