Search for H-Bonded Motifs in Liquid Ethylene Glycol Using a Machine Learning Strategy.

Abstract

Trajectories of atomic positions derived from ab initio molecular dynamics (AIMD) simulations of H-bonded liquids contain a wealth of information on dominant structural motifs and recurrent patterns of association. Extracting this information from a detailed search of the trajectories over multiple time frames is, however, a daunting exercise. Here, we use a machine learning strategy based on the neural inspired approach of the self-organizing maps (SOM), a type of artificial neural network that uses unsupervised competitive learning, to analyze the AIMD trajectories of liquid ethylene glycol (EG). The objective was to find whether there are H-bonded fragments, of two or more H-bonded EG molecules, that are recurrent in the liquid and to identify them. The SOM represents a set of high-dimensional data mapped onto a two-dimensional, grid of neurons or nodes, while preserving the topological properties of the input space. We show here that clustering of the fragments by SOM in terms of the molecular conformation of the individual EG molecules of the fragment and their H-bond connectivity pattern facilitates the search for H-bonded motifs. Using this approach, we are able to identify a H-bonded cyclic dimer and a bifurcated H-bonded structure as recurring motifs that appear in the longer H-bonded fragments present in liquid EG.