Gareth A. Tribello

A new approach is proposed for exploring the low-energy structures of small to medium-sized aggregates of atoms and molecules. This approach uses the recently proposed reconnaissance metadynamics method [G. A. Tribello, M. Ceriotti, and M. Parrinello. Proc. Natl. Acad. Sci. U.S.A. 107(41), 17509 (2010)10.1073/pnas.1011511107] in tandem with collective variables that describe the average structure of the coordination sphere around the atoms/molecules. We demonstrate this method on both Lennard-Jones and water clusters and show how it is able to quickly find the global minimum in the potential energy surface, while exploring the finite temperature free energy surface.

A new scheme, sketch-map, for obtaining a low-dimensional representation of the region of phase space explored during an enhanced dynamics simulation is proposed. We show evidence, from an examination of the distribution of pairwise distances between frames, that some features of the free-energy surface are inherently high-dimensional. This makes dimensionality reduction problematic because the data does not satisfy the assumptions made in conventional manifold learning algorithms We therefore propose that when dimensionality reduction is performed on trajectory data one should think of the resultant embedding as a quickly sketched set of directions rather than a road map. In other words, the embedding tells one about the connectivity between states but does not provide the vectors that correspond to the slow degrees of freedom. This realization informs the development of sketch-map, which endeavors to reproduce the proximity information from the high-dimensionality description in a space of lower dimensionality even when a faithful embedding is not possible.

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

The precipitation of calcium carbonate in water has been examined using a combination of molecular dynamics and umbrella sampling. During 20 ns molecular dynamics trajectories at elevated calcium carbonate concentrations, amorphous particles are observed to form and appear to be composed of misaligned domains of vaterite and aragonite. The addition of further calcium ions to these clusters is found to be energetically favorable and virtually barrierless. By contrast, there is a large barrier to the addition of calcium to small calcite crystals. Thus, even though calcite nanocrystals are stable in solution, at high supersaturations, particles of amorphous material form because this material grows much faster than ordered calcite nanocrystals.

Polyacrylate molecules can be used to slow the growth of calcium carbonate. However, little is known about the mechanism by which the molecules impede the growth rate. A recent computational study (Bulo et al. Macromolecules2007, 40, 3437) used metadynamics to investigate the binding of calcium to polyacrylate chains and has thrown some light on the coiling and precipitation of these polymers. We extend these simulations to examine the binding of calcium and carbonate to polyacrylate chains. We show that calcium complexed with both carbonate and polyacrylate is a very stable species. The free energies of calcium−carbonate−polyacrylate complexes, with different polymer configurations, are calculated, and differences in the free energy of the binding of carbonate are shown to be due to differences in the amount of steric hindrance about the calcium, which prevents the approach of the carbonate ion.