Jan Hermann
Principal Research Manager, AI For Science at Microsoft
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolJan Hermann is a Principal Research Manager at Microsoft with 12 years of experience blending computational chemistry, physics, and machine learning to accelerate AI-for-science research. Trained as a computational chemist and physicist (PhD, summa cum laude), he has led and published work on deep-learning approaches for electronic structure and quantum Monte Carlo, including papers in Nature Chemistry and Physical Review Letters. He pairs hands-on open-source engineering—contributions to prominent projects like DFTB+ and PySCF, where he implemented and refactored many-body dispersion modules—with strategic research leadership across academia and industry. Based in Berlin, Jan is known for translating complex many-body physics into robust software and novel ML models, and for smoothing cross-platform interoperability issues (e.g., MacOS compilation fixes) that improve reproducibility. Colleagues rely on him to bridge theory, scalable implementation, and practical diagnostics in atomistic simulation stacks.
12 years of coding experience
10 years of employment as a software developer
PhD, Physics, summa cum laude, PhD, Physics, summa cum laude at Humboldt-Universität zu BerlinMS, Molecular Modelling, MS, Molecular Modelling at Charles University
czech/slovak, German, English
Stackoverflow
1,056reputation
44kreached
4answers
5questions
jenkins
top-5%
Github Skills (23)
quantum-mechanics10
quantum-chemistry10
python10
fortran10
simulate9
atomics9
simulation9
numpy9
simulations9
atomic9
jenkins9
macos8
cprogramming-language7
vim6
stdin6
Programming languages (25)
Github contributions (5)
dftbplus/dftbplus
Apr 2018 - Feb 2021
DFTB+ general package for performing fast atomistic simulations
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 56 commits, 4 PRs in 2 years 10 months
Contributions summary:Jan primarily contributed to the integration and development of a many-body dispersion (MBD) model within the DFTB+ package, indicated by the frequent commits referencing "Libmbd". They refactored existing code, updated the Libmbd version, and simplified the interface between the DFTB+ code and the MBD library. Furthermore, the user added error handling and implemented a DFTB printer for the Libmbd module, enhancing the robustness and diagnostic capabilities of the dispersion calculations.
quantum-mechanicssimulationsatomistic-simulationssimulationdftb
pyscf/pyscf
May 2017 - Feb 2020
Python module for quantum chemistry
Role in this project:Back-end Developer
Back-end DeveloperContributions:14 commits, 8 PRs, 10 comments in 2 years 9 months
Contributions summary:Jan contributed to the PySCF project, a Python module for quantum chemistry. Their work included adding functionality to handle None/null values in the `ndpointer` function, which likely enhanced interoperability with other libraries. They also introduced a "first working draft" of a module related to many-body dispersion (MBD) calculations, demonstrating the development of new features for the project. Additionally, the user made code changes to address compilation issues on MacOS and updated the interface for the pyberny geometry optimizer.
pythonchemistryquantum-computingpython-modulequantum-chemistry
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