2025-07-10 –, Room 315
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.
Understanding how materials behave at the atomic level is crucial for designing new technologies, but it's incredibly challenging. Predicting chemical reactions requires solving complex quantum mechanical equations, which are computationally expensive, limiting simulations to a few hundred atoms at most. This makes it difficult to compare theoretical predictions with real-world experimental observations. Simpler methods exist, but they often lack the accuracy needed to understand the crucial details of bond formation and breakage.
Scientific Python has revolutionized materials science. Packages like NumPy, SciPy, and Matplotlib, along with specialized tools like ASE (for manipulating atomic structures) and MDAnalysis (for analyzing simulations), empower researchers to tackle this problem across vastly different scales. We can now simulate everything from massive systems using simplified models to highly accurate, but computationally expensive, methods like Density Functional Theory (DFT).
A promising new approach uses Machine Learning Interatomic Potentials (MLIPs). These learn from detailed DFT calculations to create fast simulations of large systems, but often lack the electronic structure information needed for detailed comparison with experiments. My research addresses this limitation by leveraging Density Functional Tight Binding (DFTB), a computationally efficient method that retains essential information about the electrons.
In this talk, I will present my work on DFTB, demonstrating how its power to bridge the gap between theory and experiment was only possible thanks to scientific Python tools. To further promote its use, we've created a public GitHub repository (https://github.com/Voss-Lab/SK_repository) containing parameters for a wide range of materials, making it easier to compute electronic properties and compare them directly with experimental data such as XPS measurements.
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.