Filippo Balzaretti
Postdoctoral fellow at Stanford / SLAC National Laboratory with expertise in ab-initio computational modeling, surface science simulations, and machine learning techniques. Passionate about advancing interdisciplinary approaches to materials science and energy conversion, leveraging principles from conventional algorithms and Artificial Intelligence frameworks. Excited about developing new quantum chemistry methods to model materials properties and validate them through collaborative experimental work.
Sessions
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.