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 a new class of approaches for enhancing sampling and mapping out the free energy landscape of systems described by rough potential energy surfaces.  The new techniques are based on a molecular dynamics calculation in which a set of pre-selected collective variables (CVs) is singled out for enhanced sampling.  The enhancement is achieved by applying a high temperature to the CVs and decoupling them adiabatically from the remaining degrees of freedom.  This framework also allows incorporation of bias potentials for increased efficiency.  From such calculations, issues concerning the construction of high-dimensional free energy hypersurfaces will be discussed.  Illustrative applications will include the structure of small peptides, determining polymorphs of several selected organic molecular crystals, and determination of pathways for solid to liquid phase transitions.

Algorithms for the exploration of rugged energy landscapes and construction of free energy hypersurfaces in complex systems

248th National Meeting (2014)

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