Christoph Dellago

Transition path sampling and the calculation of rate constants

We have developed a method to study transition pathways for rare events in complex systems. The method can be used to determine rate constants for transitions between stable states by turning the calculation of reactive flux correlation functions into the computation of an isomorphic reversible work. In contrast to previous dynamical approaches, the method relies neither on prior knowledge nor on explicit specification of transition states. Rather, it provides an importance sampling from which transition states can be characterized statistically. A simple model is analyzed to illustrate the methodology.

Accurate determination of crystal structures based on averaged local bond order parameters

Local bond order parameters based on spherical harmonics, also known as Steinhardt order parameters, are often used to determine crystal structures in molecular simulations. Here we propose a modification of this method in which the complex bond order vectors are averaged over the first neighbor shell of a given particle and the particle itself. As demonstrated using soft particle systems, this averaging procedure considerably improves the accuracy with which different crystal structures can be distinguished.

Efficient transition path sampling: Application to Lennard-Jones cluster rearrangements

We develop an efficient Monte-Carlo algorithm to sample an ensemble of stochastic transition paths between stable states. In our description, paths are represented by chains of states linked by Markovian transition probabilities. Rate constants and mechanisms characterizing the transition may be determined from the path ensemble. We have previously devised several algorithms for sampling the path ensemble. For these algorithms, the numerical effort scales with the square of the path length. In the new simulation scheme, the required computation scales linearly with the length of the transition path. This improved efficiency allows the calculation of rate constants in complex molecular systems. As an example, we study rearrangement processes in a cluster consisting of seven Lennard-Jones particles in two dimensions. Using a quenching technique we are able to identify the relevant transition mechanisms and to locate the related transition states. We furthermore calculate transition rate constants for various isomerization processes.

On the calculation of reaction rate constants in the transition path ensemble

We present improved formulas for the calculation of transition rate constants in the transition path ensemble. In this method transition paths between stable states are generated by sampling the distribution of paths with a Monte Carlo procedure. With the new expressions the computational cost for the calculation of transition rate constants can be reduced considerably compared to our original formulation. We demonstrate the method by studying the isomerization of a diatomic molecule immersed in a Weeks–Chandler–Andersen fluid. The paper is concluded by an efficiency analysis of the path sampling algorithm.

Transition Path Sampling

Often, the dynamics of complex condensed materials is characterized by the presence of a wide range of different time scales, complicating the study of such processes with computer simulations. Consider, for instance, dynamical processes occurring in liquid water. Here, the fastest molecular processes are intramolecular vibrations with periods in the 10–20 fs range. The translational and rotational motions of water molecules occur on a significantly longer time scale. Typically, the direction of translational motion of a molecule persist for about 500 fs, corresponding to 50 vibrational periods. Hydrogen bonds, responsible for many of the unique properties of liquid water, have an average lifetime of about 1 ps and the rotational motion of water molecules stays correlated for about 10 ps. Much longer time scales are typically involved if covalent bonds are broken and formed. For instance, the average lifetime of a water molecule in liquid water before it dissociates and forms hydroxide and hydronium ions is on the order of 10 h. This enormous range of time scales, spanning nearly 20 orders of magnitude, is a challenge for the computer simulator who wants to study such processes.

Melting of icosahedral gold nanoclusters from molecular dynamics simulations

Molecular dynamics simulations show that gold clusters with about 600-3000 atoms crystallize into a Mackay icosahedron upon cooling from the liquid. A detailed surface analysis shows that the facets on the surface of the Mackay icosahedral gold clusters soften but do not premelt below the bulk melting temperature. This softening is found to be due to the increasing mobility of vertex and edge atoms with temperature, which leads to inter-layer and intra-layer diffusion, and a shrinkage of the average facet size, so that the average shape of the cluster is nearly spherical at melting.

Sampling ensembles of deterministic transition pathways

We extend the method of transition-path sampling to the case of deterministic dynamics. This method is a Monte Carlo procedure for sampling the ensemble of trajectories that carry a many-particle system from one set of stable or metastable states to another. It requires no preconceived notions of transition mechanisms or transition states. Rather, it is from the resulting set of suitably weighted dynamical transition paths that one identifies transition mechanisms, determines relevant transition states and calculates transition rate constants. In earlier work, transition-path sampling was considered in the context of stochastic dynamics. Here, the necessary modifications that make it applicable to deterministic dynamics are discussed and the modifications illustrated with microcanonical simulations of isomerization events in two-dimensional seven-atom Lennard-Jones clusters.

Transition path sampling and other advanced simulation techniques for rare events

Computer simulations of molecular processes such as nucleation in first-order phase transitions or the folding of a protein are often complicated by widely disparate time scales related to important but rare events. Here, we will review sev eral recently developed computational methods designed to address the rare-events problem. In doing so, we will focus on the transition path sampling methodology.

Transition path sampling: throwing ropes over mountains in the dark

Understanding rare transitions occurring in complex systems, for instance chemical reactions in solution, poses the problem of finding and analysing the trajectories that move from one basin of attraction to another on a complicated potential energy surface. We have developed a systematic approach for finding these trajectories using computer simulations without preconceived knowledge of transition states. The approach follows from a novel statistical mechanics and thermodynamics of trajectories and has been demonstrated with several applications.

Macroscopically ordered water in nanopores

Water confined into the interior channels of narrow carbon nanotubes or transmembrane proteins forms collectively oriented molecular wires held together by tight hydrogen bonds. Here, we explore the thermodynamic stability and dipolar orientation of such 1D water chains from nanoscopic to macroscopic dimensions. We show that a dipole lattice model accurately recovers key properties of 1D confined water when compared to atomically detailed simulations. In a major reduction in computational complexity, we represent the dipole model in terms of effective Coulombic charges, which allows us to study pores of macroscopic lengths in equilibrium with a water bath (or vapor). We find that at ambient conditions, the water chains filling the tube are essentially continuous up to macroscopic dimensions. At reduced water vapor pressure, we observe a 1D Ising-like filling/emptying transition without a true phase transition in the thermodynamic limit. In the filled state, the chains of water molecules in the tube remain dipole-ordered up to macroscopic lengths of ≈0.1 mm, and the dipolar order is estimated to persist for times up to ≈0.1 s. The observed dipolar order in continuous water chains is a precondition for the use of nanoconfined 1D water as mediator of fast long-range proton transport, e.g., in fuel cells. For water-filled nanotube bundles and membranes, we expect anti-ferroelectric behavior, resulting in a rich phase diagram similar to that of a 2D Coulomb gas.