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The concept of chemical bonding can ultimately be seen as a rationalizationof the recurring structural patterns observed in molecules and solids. Chemicalintuition is nothing but the ability to recognize and predict such patterns,and how they transform into one another. Here we discuss how to use a computerto identify atomic patterns automatically, so as to provide an algorithmicdefinition of a bond based solely on structural information. We concentrate inparticular on hydrogen bonding -- a central concept to our understanding of thephysical chemistry of water, biological systems and many technologicallyimportant materials. Since the hydrogen bond is a somewhat fuzzy entity thatcovers a broad range of energies and distances, many different criteria havebeen proposed and used over the years, based either on sophisticate electronicstructure calculations followed by an energy decomposition analysis, or onsomewhat arbitrary choices of a range of structural parameters that is deemedto correspond to a hydrogen-bonded configuration. We introduce here adefinition that is univocal, unbiased, and adaptive, based on ourmachine-learning analysis of an atomistic simulation. The strategy we proposecould be easily adapted to similar scenarios, where one has to recognize orclassify structural patterns in a material or chemical compound.

Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond