In our team, we tackle challenges in machine learning, uncertainty quantification and high performance computing. We perform research at the overlap of computer science and applied mathematics with special emphasis on methods and applications targeted towards engineering, natural science, medicine and beyond.
Feel free to explore our recent research and teaching on https://www.peter-zaspel.de
We are continuously looking for talented PhD candidates and Postdocs. Please apply!
Scripts for various things related to "CheMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules". includes MFML learning curves, ORCA scripts etc.
Code and data to accompany mnauscript titled "Optimized Multi-Fidelity Machien Learning for Quantum Chemistry"
Scripts for MFDeltaML and data efficiency benchmarks of DeltaML, MFML, o-MFML, MFDeltaML, o-MFDeltaML on the CheMFi dataset.
Scripts for multifidelity models used to assess data hierarchy scaling for the prediction of excitation energies of molecules. This also includes scripts to generate the newly introduced Gamma curve.
Non nested training data approach to multifidelity machine learning. Includes scripts run to test this on the CheMFi dataset.
Code repository for the paper "Multi-Fidelity Machine Learning for Molecular Excitation Energies" - Authors: Vinod, Vivin; Maity, Sayan; Zaspel, Peter; Kleinekathöfer, Ulrich
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