DFTK: A Julian approach for simulating electrons in solids

Abstract

Density-functional theory (DFT) is a widespread method for sim- ulating the quantum-chemical behaviour of electrons in matter. It provides a first-principles description of many optical, me- chanical and chemical properties at an acceptable computational cost [16, 2, 3]. For a wide range of systems the obtained predic- tions are accurate and shortcomings of the theory are by now well-understood [2, 3]. The desire to tackle even bigger systems and more involved materials, however, keeps posing novel challenges that require methods to constantly improve. One example are so- called high-throughput screening approaches, which are becoming prominent in recent years. In these techniques one wishes to sys- tematically scan over huge design spaces of compounds in order to identify promising novel materials for targeted follow-up investi- gation. This has already lead to many success stories [14], such as the discovery of novel earth-abundant semiconductors [11], novel light-absorbing materials [20], electrocatalysts [8], materials for hydrogen storage [13] or for Li-ion batteries [1]. Keeping in mind the large range of physics that needs to be covered in these studies as well as the typical number of calculations (up to the order of millions), a bottleneck in these studies is the reliability and performance of the underlying DFT codes. To tackle these aspects multidisciplinary collaboration with mathematicians developing more numerically stable algorithms, computer scientists providing high-performance implementations, physicists and chemists designing appropriate models, and appli-cation scientists integrating the resulting methods inside a suitable simulation workflow is essential. While to date already a size-able number of DFT codes exist, e.g. ABINIT [19], Quantum- Espresso [6] or VASP [15] to name only a few, they lack sufficient flexibility inside their low-level computational routines to easily support fundamental research in computer science or mathematics. To test