bibliography.bib

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%% http://bibdesk.sourceforge.net/


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@string{acmcs = {ACM Comp. Surv.}}

@string{acmtoplas = {ACM Trans. on Programming Languages and Systems}}

@string{addressvpi = {Blacksburg, VA  24061-0106}}

@string{as = {Applied Statistics}}

@article{plumed2,
  author={Gareth A. Tribello and Massimiliano Bonomi and Davide Branduardi and Carlo Camilloni and Giovanni Bussi},
  title={PLUMED 2: New feathers for an old bird},
  journal={Comput.~Phys.~Commun.},
  volume=185,
  number=2,
  pages={604--613},
  year=2014
}


@webpage{grometa}

@article{Vymetal:2011gv,
author = {Vym{\v e}tal, Ji{\v r}{\'\i} and Vondr{\'a}{\v s}ek, Ji{\v r}{\'\i}},
title = {{Gyration- and Inertia-Tensor-Based Collective Coordinates for Metadynamics. Application on the Conformational Behavior of Polyalanine Peptides and Trp-Cage Folding}},
journal = {J. Phys. Chem. A},
year = {2011},
pages = {110930112611005},
}

@article{tribello-recon,
   author = {Gareth A. Tribello and Michele Ceriotti and Michele Parrinello}, 
   title = {{A self-learning algorithm for biased molecular dynamics}}, 
   volume = {107}, 
   number = {41}, 
   pages = {17509-17514}, 
   year = {2010}, 
   journal = {Proc. Natl. Acad. Sci. USA}
}

@article{field-cvs,
author = {Tribello, Gareth A. and Ceriotti, Michele and Parrinello, Michele},
title = {Using sketch-map coordinates to analyze and bias molecular dynamics simulations},
volume = {109},
number = {14}, 
pages = {5196-5201},
year = {2012},
doi = {10.1073/pnas.1201152109}, 
abstract ={When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about the process of interest, serve this purpose. Because finding these CVs is the most difficult part of any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can be used to build a low-dimensional map of a phase space of high-dimensionality. In this paper we discuss how these machine-generated CVs can be used to accelerate the exploration of phase space and to reconstruct free-energy landscapes. To do so, we develop a formalism in which high-dimensional configurations are no longer represented by low-dimensional position vectors. Instead, for each configuration we calculate a probability distribution, which has a domain that encompasses the entirety of the low-dimensional space. To construct a biasing potential, we exploit an analogy with metadynamics and use the trajectory to adaptively construct a repulsive, history-dependent bias from the distributions that correspond to the previously visited configurations. This potential forces the system to explore more of phase space by making it desirable to adopt configurations whose distributions do not overlap with the bias. We apply this algorithm to a small model protein and succeed in reproducing the free-energy surface that we obtain from a parallel tempering calculation.},
URL = {http://www.pnas.org/content/109/14/5196.abstract},
eprint = {http://www.pnas.org/content/109/14/5196.full.pdf+html},
journal = {Proceedings of the National Academy of Sciences}
}

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title = {Exploring the free energy surfaces of clusters using reconnaissance metadynamics},
publisher = {AIP},
year = {2011},
volume = {135},
number = {11},
pages = {114109},
author = {Gareth A. Tribello and Jérôme Cuny and Hagai Eshet and Michele Parrinello},
journal = { J. Chem. Phys. }
}

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@article{piana08villin,
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author = "Stefano Piana and Alessandro Laio and Fabrizio Marinelli and Marleen Van Troys and David Bourry and Christophe Ampe and Jose H. C. Martins",
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volume = "375",
number = "2",
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@article{todorova09,
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year = {2006},
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abstract = {},
affiliation = {Univ Amsterdam, Amsterdam, Netherlands},
pages = {214A--214A},
year = {2007},
month = {Jan},
language = {English},
date-added = {2010-09-15 10:06:07 +0200},
date-modified = {2010-09-15 10:06:45 +0200},
pmid = {000243972401196},
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