We address the problem of efficient detection of clusters of co-moving objects in a collection of molecular trajectories which, in addition to proximity , also satisfy semantic criteria in terms of chemical properties of interest. Specifically, we are interested in the motion of atoms from different molecules that at some point of the motion form Hydrogen Bonds (HB) and that HB persists over time. While a traditional continuous spatial proximity is an important criterion, the semantic aspect of which atoms from which molecules are co-moving, and whether they are also within certain spatial bounds is just as important. It may be tempting to use existing formalisms such as flocks, convoys, swarms, etc. – however, there are notable differences due to the semantics of the chemistry of HB formation: (1) There are additional constraints within clusters; and (2) From the perspective of the chemical interactions, it is permissible that an HB within a cluster is disrupted for a brief period (i.e., the persistency of the bond can be relaxed), for as long as it is re-established again soon. To enable the detection of such phenomena in datasets of atomic trajectories, we introduce the notion of Bond-Aware Relaxed Moving Clusters (BARMC) pattern – a novel type of spatio-temporal moving cluster pertaining to molecular dynamics. We provide an algorithm for the efficient detection of BARMC patterns along with experimental evaluation demonstrating its benefits over the Naïve approach based on traditional convoys.
Shamail et al. (Tue,) studied this question.