As a demonstration of how fundamental chemical concepts can be gleaned from data using machine learning methods, we demonstrate the automated detection of hydrogen bonds by statistical analysis of molecular dynamics trajectories. In particular, we infer the existence and nature of electrostatically driven noncovalent interactions by examining the relative probability of supramolecular configurations with and without electrostatic interactions. Then, using Laplacian eigenmaps clustering, we identify hydrogen bonding motifs in hydrogen fluoride, water, and methanol. The hydrogen bonding motifs that we identify support traditional geometric criteria.
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