Electronic structure calculations, especially those using density functional theory (DFT), have been very useful in understanding and predicting a wide range of materials properties. The importance of DFT calculations to engineering and physical sciences is evident from the fact that ~20% of computational resources on some of the world’s largest public supercomputers are devoted to DFT calculations. Despite the wide adoption of DFT, and the tremendous progress in theory and numerical methods over the decades, the following challenges remain. Firstly, the state-of-the-art implementations of DFT suffer from cell-size and geometry limitations, with the widely used codes in solid state physics being limited to periodic geometries and typical simulation domains containing a few hundred atoms. This limits the complexity of materials systems that can be treated using DFT calculations. Secondly, there are many materials systems (such as strongly-correlated systems) where the widely used model exchange-correlation functionals, which account for the many-body quantum mechanical interactions between electrons, are inaccurate. Addressing these challenges will enable large-scale quantum-accuracy DFT calculations, and will significantly advance our predictive modeling capabilities to treat complex materials systems. This presentation will discuss our recent advances towards addressing the aforementioned challenges. In particular, the development of computational methods and numerical algorithms for conducting fast and accurate large-scale DFT calculations using adaptive finite-element discretization will be presented, which form the basis for the recently released DFT-FE open-source code. The computational efficiency, scalability and performance of DFT-FE will be presented, which will demonstrate a significant outperformance of widely used plane-wave DFT codes. DFT studies on dislocations and biomolecules will be presented to showcase the capability of DFT-FE in handling large-scale systems. Finally, recent efforts, and related thoughts, towards developing a framework for a data-driven approach to improve the exchange-correlation description in DFT will be also discussed.