Invited Talks

One of the computational grand challenge problems is the development of methodology capable of sampling conformational equilibria in systems characterized by rough energy landscapes. If met, many important problems, most notably biomolecular structure prediction and the discovery of the polymorphism in organic molecular crystals could be significantly impacted. In this talk, I will discuss several approaches for enhancing sampling and mapping out the free energy landscape of systems described by rough potential energy surfaces. These include mass tensor dynamics, adiabatic dynamics, and dynamical spatial warping. Mass tensor dynamics will be used to create a hierarchy of time scales between solvent, side-chain, and backbone motion in small proteins and will be used to explore the folding of short hairprin peptides. I will show how temperature acceleration techniques combined with mass tensor dynamics can be used to predict multi-dimensional free energy surfaces, and the approach will be show to enhance sampling in a variety of biomolecular systems. A similar approach will also be shown to enhance the sampling of the space of polymorphs of molecular crystals. Finally, I will describe a new approach in which molecular dynamics is combined with a novel variable transformation designed to warp configuration space in such a way that barriers are reduced and attractive basins stretched. The new method rigorously preserves equilibrium properties while leading to very large enhancements in sampling efficiency. The performance of the method is demonstrated on long polymer chains and simple protein models and is shown to significantly outperform replica-exchange Monte Carlo with only one trajectory.