Opportunities for Simulating Large Systems using Accurate and Efficient Density Functional Theory Calculations

Thanks to a combination of the availability of petascale supercomputers and algorithmic developments such as linear scaling density functional theory, in recent years it has become possible to perform quantum mechanical (QM) simulations for systems containing several thousand atoms. This not only opens up new possibilities for treating large and complex systems using QM, but also enables the direct comparison with non-QM methods such as force fields or coarse graining methods, at the (large) length scales at which they are typically applied. To this end, it is desirable to have a means of reducing the effective complexity of a QM simulation, i.e. to reduce the number of degrees of freedom without a significant loss of accuracy or the introduction of any bias. For such a complexity reduction, it is necessary to identify other analysis schemes for understanding and interpreting the behaviour of a QM system, for example by partitioning a large system into objects, or fragments, of a smaller size. In this talk we will discuss the opportunities for large scale QM simulations, and introduce a quantitative method for assessing the partitioning of a system into fragments, presenting examples of how such a partitioning might be exploited.