Automatic method for identifying reaction coordinates in complex systems

To interpret simulations of a complex system to determine the physical mechanism of a dynamical process, it is necessary to identify the small number of coordinates that distinguish the stable states from the transition states. We develop an automatic method for identifying these degrees of freedom from a database of candidate physical variables. In the method neural networks are used to determine the functional dependence of the probability of committing to a stable state (committor) on a set of coordinates, and a genetic algorithm selects the combination of inputs that yields the best fit. The method enables us to obtain the first set of coordinates that is demonstrably sufficient to specify the transition state of the C(7eq)--> alpha(R) isomerization of the alanine dipeptide in the presence of explicit water molecules. It is revealed that the solute-solvent coupling can be described by a solvent-derived electrostatic torque around one of the main-chain bonds, and the collective, long-ranged nature of this interaction accounts for previous failures to characterize this reaction.