The BigDFT project started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn-Sham density functional theory with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features for exibility, performance and precision of the results. In particular, the employed formalism has enabled the implementation of an algorithm able to tackle DFT calculations of large systems, up to many thousands of atoms, with a computational eort which scales linearly with the number of atoms. In this work we recall some of the features that have been made possible by the peculiar properties of Daubechies wavelets. In particular, we focus our attention on the usage of DFT for large-scale systems. We show how the localised description of the KS problem, emerging from the features of the basis set, are helpful in providing a simplied description of large-scale electronic structure calculations. We provide some examples on how such simplied description can be employed, and we consider, among the case-studies, the SARS-CoV-2 main protease.

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In this contribution we will describe in detail a Density Functional Theory method based on a Daubechies wavelets basis set, named BigDFT. We will see that, thanks to wavelet properties, this code shows high systematic convergence properties, very good performances and an excellent efficiency for parallel calculations. BigDFT code operation are also well-suited for GPU acceleration. We will discuss how the problematic of fruitfully benefit of this new technology can be match with the needs of robustness and flexibility of a complex code like BigDFT. This work may be of interest not only for expert in electronic structure calculations, but may also provide feedback to the wider community of high performance scientific computing.