Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

Automated summary

This review provides a comprehensive overview of the importance of unsupervised learning in analyzing data from atomistic and molecular simulations, discussing state-of-the-art algorithms and methods in feature representation, dimensionality reduction, density estimation, clustering, and kinetic models. The article is well-structured, with detailed discussions in each section on the mathematical and algorithmic foundations, strengths and limitations of each method, and specific applications in analyzing molecular simulation data.