DOE OSTI2012
Most common atomistic simulation techniques, like molecular dynamics or Metropolis Monte Carlo, operate under a constant interatomic Hamiltonian with a fixed number of atoms. Internal (atom positions or velocities) or external (simulation cell size or geometry) variables are then evolved dynamically or stochastically to yield sampling in different ensembles, such as microcanonical (NVE), canonical (NVT), isothermal-isobaric (NPT), etc. Averages are then taken to compute relevant physical properties. At least two limitations of these standard approaches can seriously hamper their application to many important systems: (1) they do not allow for the exchange of particles with a reservoir, and (2) the sampling efficiency is insufficient to allow the obtention of converged results because of the very long intrinsic timescales associated with these quantities. To fix ideas, one might want to identify low (free) energy configurations of grain boundaries (GB). In reality, grain boundaries are in contact the grains which act as reservoirs of defects (e.g., vacancies and interstitials). Since the GB can exchange particles with its environment, the most stable configuration cannot provably be found by sampling from NVE or NVT ensembles alone: one needs to allow the number of atoms in the sample to fluctuate. The first limitation can be circumvented by working in the grand canonical ensemble (TV ) or its derivatives (such as the semi-grand-canonical ensemble useful for the study of substitutional alloys). Monte Carlo methods have been the first to adapt to this kind of system where the number of atoms is allowed to fluctuate. Many of these methods are based on the Widom insertion method [Widom63] where the chemical potential of a given chemical species can be inferred from the potential energy changes upon random insertion of a new particle within the simulation cell. Other techniques, such as the Gibbs ensemble Monte Carlo [Panagiotopoulos87] where exchanges of particles are attempted to equilibrate the chemical potential between two cells and hence allow for the calculation of coexistence curves, exploit the same idea: particle insertion (or exchange) is attempted and accepted with a Metropolis-like rule that depends exponentially on the energy change upon insertion. A well known limitation of this kind of approach is that the probability of accepting such a move decreases extremely rapidly with increasing density, due to the extremely large short-range repulsion between atoms. In response to these difficulties it became apparent that a solution to the problem might be to avoid abrupt insertions but instead to proceed gradually, so as to allow the system to react and make way for the incoming particle. In this view of things, the 'occupation' of a certain atomic site can be viewed as a continuous variable, ranging between 0 and 1, representing 'how much' of the particle is present at any given time. These ideas proved ideal in Molecular Dynamics (MD) settings because equations of motions for these occupation variables can sometimes be obtained. For example, in the case of Grand Canonical Molecular Dynamics [Cagin91], one special particle is allowed to have a fractional occupation. This can lead to either its destruction (occupation = 0) or its complex creation (occupation = 1) so as to enforce a given chemical potential. These approaches proved useful, but mostly in the liquid state where the probability of successfully inserting a new particle is sufficiently high. At higher densities, convergence proved to be hampered by very inefficient sampling. In this work, we explore the use of a related MD-based grand canonical technique, the Grand Canonical Simulated Quenching (GCSQ) of Phillpot and Rickman [Phillpot92,Phillpot94], and explore its application to the grand canonical sampling of solid state systems. We show that, in conjunction with advanced sampling techniques, GCSQ can be a useful tool to sample conformations of complex systems, such as GBs, and assist in the identification of their most stable states and/or most likely defective states.