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WIREs Comput Mol Sci
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New advances in metadynamics

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Abstract Metadynamics is an algorithm for accelerating rare events and reconstructing the associated free energy surface. It works by biasing the evolution of the system by a history‐dependent potential that is adaptively constructed in the space of a suitably chosen set of collective variables. Since its first appearance, the method has been successfully applied in several domains of science. Its widespread adoption is not only due to its efficiency, flexibility, and availability but also to its continuous evolution and its combination with complementary enhanced sampling algorithms. Here, we focus on the progress made in the development of more general and powerful collective variables and on the very recent and exciting evolutions of the method. © 2012 John Wiley & Sons, Ltd. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

Example of convergence failure due to the presence of a hidden, slow varying, variable. (a) Model two‐dimensional potential showing significant energy barriers in CV1 and CV2 (CV, collective variable). (b) In a metadynamics simulation biasing only CV1, the bias potential will display hysteresis as attested by the effective free energy profile along CV1 at different times. The bias deposed at different time‐slices is represented by different colors.

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Evolution of the bias of a reconnaissance metadynamics run on a bidimensionalpotential energy surface. The black dots indicate the positions accumulated from the trajectory. The red ellipses indicate the basins found using the clustering algorithm. Blue ellipses are those basins to which hills are being added.

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Free energy surface as obtained from a metadynamics simulation using as collective variables the projection along the first two principal components (v1 and v2) of the met‐enkephalin peptide.35

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