One of the computational grand challenge problems is to develop methodology capable of sampling conformational equilibria in systems with rough energy landscapes. If met,
many important problems, most notably biomolecular structure prediction and the discovery of the polymorphs of organic molecular crystals could be significantly impacted. In this talk, I will discuss several new approaches for enhancing sampling and exploring the free energy landscape of systems with rough potential energy surfaces. These include mass tensor dynamics, adiabatic dynamics, 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 a -hairprin peptide. I will show how temperature accleration techniques can be used to predict multi-dimensional free energy surfaces, and the approach will be shown to enhance sampling in a variety of biomolecular systems. A new version of adiabatic dynamics adapted for the isothermal-isobaric ensemble
will 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.