Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Abrams, Cameron F.; Bussi, Giovanni

doi:10.3390/e16010163

Cited by 395 publications

(415 citation statements)

References 250 publications

(330 reference statements)

Supporting

Mentioning

413

Contrasting

Order By: Relevance

“…This list is by no means exhaustive; for a more thorough review see for example ref. 22, but the sheer number of attempts to solve this problem testifies to its importance.…”

Section: Significancementioning

confidence: 99%

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Shaffer

Valsson

Parrinello

2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute freeenergy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.enhanced sampling | protein folding | free-energy calculation | biomolecular simulation M olecular simulation has become an increasingly useful tool for studying complex transformations in chemistry, biology, and materials science. Such simulations provide extremely high spatial and temporal resolution but suffer from inherent time-scale limitations. Molecular dynamics simulations of biomolecules performed on standard hardware struggle to exceed the microsecond time scale. Modern, purpose-built hardware can reach the millisecond time scale (1), but these tools are not generally available and they still prove insufficient for many important problems (2). Widely used enhanced sampling methods also allow one to simulate processes with a time scale of milliseconds (3) and there is significant interest in extending these methods. One important class of enhanced sampling methods involves applying an external bias to one or more collective variables (CVs) of the system. Umbrella sampling and metadynamics fall into this category (4, 5), but there are many other examples (6-13). These methods are most effective when the transformations of interest are in some sense collective and can be described by a very small number of CVs.When the choice of CV is not obvious or the free-energy landscape cannot be projected meaningfully on a low-dimensional manifold, new strategies are needed. Here we describe an enhanced sampling strategy that allows one to bias high-dimensional freeenergy landscapes by exploiting the flexibility inherent in the recently introduced variationally enhanced sampling (VES) method (14). This method casts free-energy computation as a functional minimization problem, and it solves two important problems associated with high-dimensional free-energy computation. The first problem is that when exploring a high-dimensional space a system might never visit the regions of interest, but the variational principle introduced in ref. 14 permits us to specifically target the region we are interested in through the use of a so-called "target distribution." The second problem is that one must adopt an approximation for the high-dimensional bias and the variational principle allows us to do this in an optimal way. Furthermore, as we show here, for the first time to our knowledge, choos...

show abstract

“…This list is by no means exhaustive; for a more thorough review see for example ref. 22, but the sheer number of attempts to solve this problem testifies to its importance.…”

Section: Significancementioning

confidence: 99%

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Shaffer

Valsson

Parrinello

2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…[74,84] As such, these reaction steps are rare events and their probability of occurring during a standard AIMD simulation is relatively low. To enhance sampling of interesting regions on the FES, a multitude of methods has been developed, [37,85,86] among which the metadynamics method, developed by Laio and Parrinello, [34,35,[87][88][89][90] is very promising to study zeolitecatalyzed reactions. [42,44] The method is especially suited to explore new reaction pathways by identifying a set of collective variables (CVs) that allow sampling interesting regions of the potential and free energy surface.…”

Section: Case Study Ii: Simulating Competing Pathways For Benzene Metmentioning

confidence: 99%

Complex Reaction Environments and Competing Reaction Mechanisms in Zeolite Catalysis: Insights from Advanced Molecular Dynamics

Wispelaere

Ensing

Ghysels

et al. 2015

Chemistry A European J

View full text Add to dashboard Cite

The methanol to olefins process is a show case example of complex zeolite-catalyzed chemistry. At real operating conditions, many factors such as framework flexibility, adsorption of various guest molecules and competitive reaction pathways, affect reactivity. In this paper we show the strength of first principle molecular dynamics techniques to capture this complexity by means of two case studies. Firstly, the adsorption behavior of methanol and water in H-SAPO-34 at 350 °C is investigated. Hereby we observed an important degree of framework flexibility and proton mobility. Secondly, we studied the methylation of benzene by methanol via a competitive direct and stepwise pathway in the AFI topology. Both case studies clearly show that a first principle molecular dynamics approach enables to obtain unprecedented insights into zeolite-catalyzed reactions at the nanometer scale.Keywords: ab initio calculations, molecular dynamics, proton mobility, methylation, zeolites 3 IntroductionChemical conversions in zeolites play an essential role in today's industrial catalysis. [1][2][3] Within the field of heterogeneous catalysis, the conversion of methanol to hydrocarbons (MTH) or olefins (MTO) over acidic zeolites received a lot of attention during the last decades, due to its relevance in the search for alternative processes to produce hydrocarbon products. [4][5][6][7] The MTO process has experimentally been developed in the past 3-4 decades and is currently being industrialized. [5] Methanol can be obtained from coal, natural gas or biomass and is in turn converted into ethene, propene or other hydrocarbons. The process proved extremely difficult to unravel at the nanoscale level due to various factors such as pressure, temperature and the occurrence of competing reaction mechanisms, which highly influence the catalytic performance of the system. Due to its complexity, it is a very challenging case study for theoretical modeling studies. [8] Currently, there is a consensus that a hydrocarbon pool (HP) mechanism operates during the methanol conversion process. Herein, an organic center is trapped in the zeolite pores and acts as co-catalyst. [9][10][11] The HP may be of aromatic or aliphatic nature and the two corresponding types of catalytic cycles are in close connection with each other, a concept that is called the dual cycle. [12] Depending on the catalyst topology and operating conditions either one or both cycles may be responsible for olefin formation during the methanol conversion process. [12][13][14] Theoretical contributions have proven to be indispensable within MTO research.Methodological developments and a steady increase in computer power, contributed to the fact that many properties, in particular rate coefficients of well-defined elementary reactions, are now routinely calculated with high accuracy. [8,[15][16][17] However, theoretical chemists are still confronted with paramount challenges to thoroughly explain experimental observations. The true challenge lies in linking the model system wit...

show abstract

“…Besides the advantages in analysing and plotting a posteriori the relevant information, CVs satisfying Eq. (1) can be used onthe-fly to bias the dynamics of the system and thus speed-up equilibrium sampling, like in the case of umbrella sampling, metadynamics and steered molecular dynamics [5][6][7], or even to obtain efficiently dynamical properties [3,8]. In the following, we shall call "reaction coordinate" any CV that satisfies Eq.…”

Section: Langevin Equation Of Kindmentioning

confidence: 99%

Properties of low-dimensional collective variables in the molecular dynamics of biopolymers

2016

View full text Add to dashboard Cite

The description of the dynamics of a complex, high-dimensional system in terms of a lowdimensional set of collective variables Y can be fruitful if the low dimensional representation satisfies a Langevin equation with drift and diffusion coefficients which depend only on Y . We present a computational scheme to evaluate whether a given collective variable provides a faithful lowdimensional representation of the dynamics of a high-dimensional system. The scheme is based on the framework of finite-difference Langevin-equation, similar to that used for molecular-dynamics simulations. This allows one to calculate the drift and diffusion coefficients in any point of the full-dimensional system. The width of the distribution of drift and diffusion coefficients in an ensemble of microscopic points at the same value of Y indicates to which extent the dynamics of Y is described by a simple Langevin equation. Using a simple protein model we show that collective variables often used to describe biopolymers display a non-negligible width both in the drift and in the diffusion coefficients. We also show that the associated effective force is compatible with the equilibrium free-energy calculated from a microscopic sampling, but results in markedly different dynamical properties. * guido.tiana@unimi.it 2

show abstract

Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Cited by 395 publications

References 250 publications

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Complex Reaction Environments and Competing Reaction Mechanisms in Zeolite Catalysis: Insights from Advanced Molecular Dynamics

Properties of low-dimensional collective variables in the molecular dynamics of biopolymers

Contact Info

Product

Resources

About