Designing tomorrow's materials requires understanding how atoms behave – a challenge that's both fascinating and incredibly complex. While machine learning offers exciting speedups in materials simulation, it often falls short, missing vital electronic structure information needed to connect theory with experimental results. This work introduces a powerful solution: Density Functional Tight Binding (DFTB), which, combined with the versatile tools of Scientific Python, allows us to understand the electronic behavior of materials while maintaining computational efficiency. In this talk, I will present our findings demonstrating how DFTB, coupled with readily available Python packages, allows for direct comparison between theoretical predictions and experimental data, such as XPS measurements. I will also showcase our publicly available repository, containing DFTB parameters for a wide range of materials, making this powerful approach accessible to the broader research community.