Scholar
Jan Hermann
Google Scholar ID: 5TjVq0YAAAAJ
Microsoft Research AI for Science
electronic structure
machine learning
chemistry
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Citations & Impact
All-time
Citations
4,559
H-index
17
i10-index
23
Publications
20
Co-authors
65
list available
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Publications
2 items
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
2025
Cited
0
Accurate and scalable exchange-correlation with deep learning
2025
Cited
0
Resume
Academic Achievements
Published numerous high-impact papers in journals such as Nature Chemistry, Nature Communications, and Journal of Chemical Physics
Seminal work: Deep-neural-network solution of the electronic Schrödinger equation (Nat. Chem. 2020, cited 712 times)
Co-developed DeepQMC, an open-source suite for variational optimization of deep-learning molecular wave functions (J. Chem. Phys. 2023)
Contributed to highly accurate neural network models for real-space electron densities (J. Chem. Phys. 2025)
Proposed a variational principle to regularize machine-learned density functionals (J. Chem. Phys. 2023)
Developed libMBD, a general-purpose package for scalable quantum many-body dispersion calculations (J. Chem. Phys. 2023)
Active in frontier research on machine learning in electronic structure, van der Waals interactions, and excited states
Co-authors
8 total
Alexandre Tkatchenko
Professor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSC
Noe Frank
Microsoft Research
Zeno Schätzle
PhD student, FU Berlin
Péter Bernát Szabó
Graduate student, Microsoft Research, FU Berlin
Antonio Mezzacapo
IBM Quantum
David Pfau
Staff Research Scientist at Google DeepMind
W. M. C. Foulkes
Imperial College London
Giuseppe Carleo
Associate Professor of Physics - EPFL, Lausanne, Switzerland