@article {2022|2161,
	title = {Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]},
	journal = {Living Journal of Computational Molecular Science},
	volume = {4},
	year = {2022},
	month = {Dec.},
	pages = {1583},
	abstract = {<p>Enhanced sampling algorithms have emerged as powerful methods to extend the utility of molecular dynamics simulations and allow the sampling of larger portions of the configuration space of complex systems in a given amount of simulation time. This review aims to present the unifying principles of and differences between many of the computational methods currently used for enhanced sampling in molecular simulations of biomolecules, soft matter and molecular crystals. In fact, despite the apparent abundance and divergence of such methods, the principles at their core can be boiled down to a relatively limited number of statistical and physical concepts. To enable comparisons, the various methods are introduced using similar terminology and notation. We then illustrate in which ways many different methods combine features of a relatively small number of the same enhanced sampling concepts. This review is intended for scientists with an understanding of the basics of molecular dynamics simulations and statistical physics who want a deeper understanding of the ideas that underlie various enhanced sampling methods and the relationships between them. This living review is intended to be updated to continue to reflect the wealth of sampling methods as they continue to emerge in the literature.</p>
},
	doi = {10.33011/livecoms.4.1.1583},
	url = {https://livecomsjournal.org/index.php/livecoms/article/view/v4i1e1583},
	author = {J{\'e}r{\^o}me H{\'e}nin and Leli{\`e}vre, Tony and Shirts, Michael R. and Valsson, Omar and Delemotte, Lucie}
}
@article {2015|1667,
	title = {The adaptive biasing force method: everything you always wanted to know but were afraid to ask.},
	journal = {J. Phys. Chem. B},
	volume = {119},
	year = {2015},
	month = {jan},
	pages = {1129{\textendash}51},
	abstract = {<p>In the host of numerical schemes devised to calculate free energy differences by way of geometric transformations, the adaptive biasing force algorithm has emerged as a promising route to map complex free-energy landscapes. It relies upon the simple concept that as a simulation progresses, a continuously updated biasing force is added to the equations of motion, such that in the long-time limit it yields a Hamiltonian devoid of an average force acting along the transition coordinate of interest. This means that sampling proceeds uniformly on a flat free-energy surface, thus providing reliable free-energy estimates. Much of the appeal of the algorithm to the practitioner is in its physically intuitive underlying ideas and the absence of any requirements for prior knowledge about free-energy landscapes. Since its inception in 2001, the adaptive biasing force scheme has been the subject of considerable attention, from in-depth mathematical analysis of convergence properties to novel developments and extensions. The method has also been successfully applied to many challenging problems in chemistry and biology. In this contribution, the method is presented in a comprehensive, self-contained fashion, discussing with a critical eye its properties, applicability, and inherent limitations, as well as introducing novel extensions. Through free-energy calculations of prototypical molecular systems, many methodological aspects are examined, from stratification strategies to overcoming the so-called hidden barriers in orthogonal space, relevant not only to the adaptive biasing force algorithm but also to other importance-sampling schemes. On the basis of the discussions in this paper, a number of good practices for improving the efficiency and reliability of the computed free-energy differences are proposed.</p>},
	issn = {1520-5207},
	doi = {10.1021/jp506633n},
	author = {Comer, Jeffrey and Gumbart, James C and J{\'e}r{\^o}me H{\'e}nin and Leli{\`e}vre, Tony and Pohorille, Andrew and Christophe Chipot}
}
