Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.05.369744v1?rss=1 Authors: Torrillo, P. A., Bogetti, A. T., Chong, L. Abstract: A promising approach for simulating rare events with rigorous kinetics is the weighted ensemble path sampling strategy. One challenge of this strategy is the division of configurational space into bins for sampling. Here we present the minimal adaptive binning (MAB) scheme for the automated, adaptive placement of bins along a progress coordinate within the framework of the weighted ensemble strategy. In this scheme, a fixed number of bins are evenly spaced between the positions of trailing and leading trajectories along a progress coordinate after each resampling interval. To further enhance the sampling of bottleneck regions, additional bins are dedicated to trajectories residing in the least visited regions of the free energy landscape. On average, bins are more finely spaced along steeper regions of the landscape and further apart along flatter regions of the landscape. We applied the scheme to simulations of the following processes, listed in order of increasing complexity: (i) transitions between states of a double-well toy potential; (ii) association of Na+ and Cl- ions; and (iii) conformational sampling of a tumor suppressor p53 peptide fragment. Results reveal that our binning scheme, despite its simplicity, efficiently estimates rate constants for rare-event processes with large free energy barriers and enhances conformational sampling. Copy rights belong to original authors. Visit the link for more info