Fabian Thiemann developed an end-to-end Geodite Graph Attention Transformer Network for atomic systems within the IBM/materials repository, focusing on modeling atomic interactions and properties for materials science applications. He designed and implemented modular encoders, decoders, and data loaders to process atomic features, laying the groundwork for training, inference, and evaluation pipelines. Fabian’s approach leveraged Python, PyTorch, and deep learning techniques, with an emphasis on graph neural networks to enable extensibility for future experiments. The work demonstrated depth in both model architecture and data handling, providing a robust foundation for ongoing research and development in computational materials science.