Abstract

Density-functional theory (DFT) is routinely used to simulate a wide variety of materials and properties, however, standard implementations of DFT are cubic scaling with the number of atoms, limiting calculations to a few hundred atoms. However, in recent years various linear scaling (LS) approaches have been developed, enabling simulations on tens of thousands of atoms. One key factor influencing the accuracy and cost of DFT is the basis set, where minimal, localized basis sets compete with extended, systematic basis sets. On the other hand, wavelets offer both locality and systematicity and are thus ideal for representing an adaptive local orbital basis which may be exploited for LS-DFT. One may also make further physically-motivated approximations, e.g. dividing a system into fragments or exploiting underlying repetition of local chemical environments, where each approximation may be controlled and quantified. This ability to treat large systems with controlled precision offers the possibility of new types of materials simulations. In this talk I will demonstrate the advantages of such an approach for large scale DFT calculations, as implemented in BigDFT. I will focus on the example of materials for organic LEDs, showing how this approach may be used to account for environmental and statistical effects on excited state calculations of disordered supramolecular materials.