MDMC2: A molecular dynamics code for investigating the fragmentation dynamics of multiply charged clusters
Résumé
MDMC2 is a parallel code for performing molecular dynamics simulations on multiply charged clusters. It is a valuable complement to MCMC2, a Monte Carlo program devoted to Monte Carlo simulations of multiply charged clusters in the NVT ensemble (Bonhommeau and Gaigeot, 2013). Both MCMC2 and MDMC2 codes employ a mesoscopic coarse-grained simplified representation of the clusters (or droplets): these clusters are composed of neutral and charged spherical particles/grains that may be polarisable. One grain can be either neutral or charged. The interaction potential is a sum of 2-body Lennard- Jones potentials (main cohesive contribution) and electrostatic terms (repulsive contribution), possibly supplemented by N-body polarisation interactions. There is no restriction imposed on the values of the particle charges and/or polarisabilities. An external field can also be applied to the whole system. The derivatives of the potential energy-surface are determined analytically which ensures an accurate integration of classical equations of motion by a velocity Verlet algorithm. Conservation rules, such as energy conservation or centre-of-mass linear momentum conservation, can be steadily checked during the simulation. The program also provides some statistical information on the run and configuration files that can be used for data post-treatment. MDMC2 is provided with a serial conjugate gradient program, called CGMC2, that uses the same analytical derivatives as MDMC2 and was found useful to probe the minima of the energy landscape explored during Monte Carlo or molecular dynamics simulations performed on multiply charged clusters.
Mots clés
Charged clusters
Charged spherical particles
Coarse grained models
Fission
Fragmentation dynamics
Molecular dynamics simulations
Statistical information
Velocity Verlet algorithm
Drops
Electrospray ionization
Equations of motion
Evaporation
Molecular dynamics
Superconducting materials
Monte Carlo methods