Advances in enhanced sampling along adaptive paths of collective variables

Study of complex activated molecular transitions by molecular dynamics (MD) simulation can be a daunting task, especially when little knowledge is available on the reaction coordinate describing the mechanism of the process. Here, we assess the path-metadynamics enhanced sampling approach in combination with force field and ab initio [density functional theory (DFT)] MD simulations of conformational and chemical transitions that require three or more collective variables (CVs) to describe the processes. We show that the method efficiently localizes the average transition path of each process and simultaneously obtains the free energy profile along the path. The new multiple-walker implementation greatly speeds-up the calculation, with an almost trivial scaling of the number of parallel replicas. Increasing the dimensionality by expanding the set of CVs leads to a less than linear increase in the computational cost, as shown by applying the method to a conformational change in increasingly longer polyproline peptides. Combined with DFT-MD to model acid (de-)protonation in explicit water solvent, the transition path and associated free energy profile were obtained in less than 100 ps of simulation. A final application to hydrogen fuel production catalyzed by a hydrogenase enzyme showcases the unique mechanistic insight and chemical understanding that can be obtained from the average transition path.