  When examining dynamics occurring at non-zero temperatures, both energy and
entropy must be taken into account while describing activated barrier crossing
events. Furthermore, good reaction coordinates need to be constructed to
describe different metastable states and the transition mechanisms between
them. Here we use a physics-based machine learning method called the State
Predictive Information Bottleneck (SPIB) to find non-linear reaction
coordinates for three systems of varying complexity. The SPIB is able to
predict correctly an entropic bottleneck for an analytical flat-energy
double-well system and identify the entropy- and energy-dominated pathways for
an analytical four-well system. Finally, for a simulation of benzoic acid
permeation through a lipid bilayer, SPIB is able to discover the entropic and
energetic barriers to the permeation process. Given these results, we thus
establish that SPIB is a reasonable and robust method for finding the important
entropy and energy/enthalpy barriers in physical systems, which can then be
used for enhanced understanding and sampling of different activated mechanisms.
