Codes used in the eMinerals project

Large-scale molecular dynamics (MD) simulations: DL_POLY

DL_POLY_3 is the UK's national flagship package for molecular dynamics (MD) simulations. It is specially designed for simulation of large systems by harnessing the power of parallel platform computers.

The package is extensively used by many researchers to carry out high-precision simulations on large systems with complex interactions unthinkable just a few years ago. It is very suitable for radiation damage simulations where the high energy impacts in materials demand large MD simulation cells. DL_POLY_3 has been routinely used to simulate RD in systems of sizes from a few thousands to a few millions of atoms. DL_POLY_3 has also become a popular choice for biochemical simualtions of large proteins with system sizes reaching a few hundreds of thousands.

DL_POLY_3 incorporates a number of key features to ensure it has a goodinterface with the eMinerals project tools, including the ability to produce XML output.

More information available here.

Surface dynamics

Despite the success in recent years of modelling surfaces, atomistic simulation studies that incorporate realistic descriptions of the interatomic forces have been restricted to considering charge neutral surfaces. The difficulty is that for charged systems the cluster methods do not treat the long ranged electrostatic interactions correctly and the periodic simulation cells have unphysical high concentrations of charged species. Thus there is a need to develop new a computer simulation code for overcoming these difficulties but which uses the advantages of each. This new approach will benefit by the close co-operation between those performing the science, computer scientists, for developing new algorithms and modellers in optimisation methods.

Order-N quantum mechanics (QM): SIESTA

The applicability of empirical interatomic forces is limited when the chemical environment around the atoms changes significantly. Recent progress in first-principles QM techniques based on density-functional theory (DFT) that scale linearly with system size (as opposed to the usual cube scaling) will enable us to face the challenges proposed here. One of the main pioneering projects is SIESTA, co-developed by Dr. Artacho (Cambridge). It has been successfully applied to for other types of studies with many hundreds of atoms. Environmental studies demand important developments to allow for systems with thousands of atoms. These include improved linear-scaling solvers with functionals that are more robust, greater optimisation of atomic basis sets, and improved parallelisation based on the algebra of sparse matrices (in collaboration with Julian Gale, Imperial College). The high energies for radiation damage will require incorporation of electronic excited states.

Monte Carlo methods

Monte Carlo is ideal for exploring many configurations of a system. Examples of our usage are to study interface states, cation ordering within minerals, and generating atomic configurations that best fit experimental data. Monte Carlo studies typically require sweeps through a range of parameters or data sets, and idealy suited to grid computing methods.

Quantum Monte Carlo

Quantum Monte Carlo (QMC) is a computational method for solving Schrödinger’s equation that does not rely on the approximations inherent in current quantum mechanical techniques such as DFT and Hartree-Fock (HF). Although DFT and HF methods have dominated computational physics and chemistry over the past 20 years or so, they are limited in that electron correlations are only approximately known. Although the correlation energy is only a small part of the total energy of a system, it can be a large part of the bonding energy and thus dominate the properties of some highly correlated systems. This can be a problem for many important environmental systems, particularly transition metals. QMC provides a direct treatment of quantum many body effects and, therefore, explicitly includes electron correlation. The price, however, is that the calculations are three orders of magnitude more computationally intensive than DFT or HF methods, which means that it is only rarely applied to complex problems. Although QMC is very computationally demanding, the algorithms are intrinsically parallel with minimal communication overhead as with all MC methods. This makes it ideal for distributed computing on PCs, with one PC orchestrating the whole simulation, which runs on the other PCs independently runs a simulation.

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