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Metadynamics (MTD; also abbreviated as METAD or MetaD) is a computer simulation method in computational physics, chemistry and biology. It is used to estimate the free energy and other state functions of a system, where ergodicity is hindered by the form of the system's energy landscape. It was first suggested by Alessandro Laio and Michele Parrinello in 2002 and is usually applied within molecular dynamics simulations. MTD closely resembles a number of newer methods such as adaptively biased molecular dynamics, adaptive reaction coordinate forces and local elevation umbrella sampling. More recently, both the original and well-tempered metadynamics were derived in the context of importance sampling and shown to be a special case of the adaptive biasing potential setting. MTD is related to the Wang–Landau sampling. Introduction The technique builds on a large number of related methods including (in a chronological order) the deflation, tunneling, tabu

DFT-Derived Reactive Potentials for the Simulation of Activated Processes: the Case of CdTe and CdTe:S

Wanda Andreoni, Riccardo Ciaglia, Fabio Pietrucci

We introduce a new ab initio derived reactive potential for the simulation of CdTe within density functional theory (DFT) and apply it to calculate both static and dynamical properties of a number of systems (bulk solid, defective structures, liquid, surfaces) at finite temperature. In particular, we also consider cases with low sulfur concentration (CdTe:S). The analysis of DFT and classical molecular dynamics (MD) simulations performed with the same protocol leads to stringent performance tests and to a detailed comparison of the two schemes. Metadynamics techniques are used to empower both Car-Parrinello and classical molecular dynamics for the simulation of activated processes. For the latter, we consider surface reconstruction and sulfur diffusion in the bulk. The same procedures are applied using previously proposed force fields for CdTe and CdTeS materials, thus allowing for a detailed comparison of the various schemes.

Amer Chemical Soc2014

Capture and Release of CO2 in Monoethanolamine Aqueous Solutions: New Insights from First-Principles Reaction Dynamics

Wanda Andreoni, Changru Ma, Fabio Pietrucci

Aqueous,monoethanolamine (MEA) solution is commonly used for post-combustion carbon capture via chemical absorption. Extensive research has been carried out to characterize both uptake and release of carbon dioxide (CO2), with the aim of improving process performance. However, an intensive research is still needed on fundamental aspects of the key chemical reactions, to achieve a comprehensive understanding of the cyclic process at the microscopic level and a quantitative assessment. We present several ab initio simulations of MBA solutions at a concentration of 30 wt % the current standard in the industry and study the dynamics of key multistep chemical reactions, using the metadynamics technique. Pathways for the entire cycle are investigated and characterized in terms of related free-energy and enthalpy barriers, and of the accompanying variations in both structural and electronic properties. The results of this study lead us to propose, among competing processes, an unforeseen scenario in which the zwitterion acts as sn intermediate not only of CO2 uptake, in the form of carbamate, but also of its release. Rate-limiting steps are the formation of the zwitterion for the former and MEAH(+) deprotonation for the latter. Water is shown to play a multifaceted role, which is crucial in determining the development and the energetics of each step of the reactions. The level of comprehension here achieved for MBA should help defining a strategy for solvent optimization.

Amer Chemical Soc2015

Iterative Unbiasing of Quasi-Equilibrium Sampling

Michele Ceriotti, Bingqing Cheng, Federico Giberti, Gareth Aneurin Tribello

Atomistic modeling of phase transitions, chemical reactions, or other rare events that involve overcoming high free energy barriers usually entails prohibitively long simulation times. Introducing a bias potential as a function of an appropriately chosen set of collective variables can significantly accelerate the exploration of phase space, albeit at the price of distorting the distribution of microstates. Efficient reweighting to recover the unbiased distribution can be nontrivial when employing adaptive sampling techniques such as metadynamics, variationally enhanced sampling, or parallel bias metadynamics, in which the system evolves in a quasi-equilibrium manner under a time-dependent bias. We introduce an iterative unbiasing scheme that makes efficient use of all the trajectory data and that does not require the distribution to be evaluated on a grid. The method can thus be used even when the bias has a high dimensionality. We benchmark this approach against some of the existing schemes on model systems with different complexity and dimensionality.

AMER CHEMICAL SOC2020