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Jan Hermann
Jan Hermann
Computational chemist and physicist by training️, who got later hooked on machine learning, and now enjoys integrating deep learning into quantum chemistry. I was born and raised in Český Krumlov, moved for university to Prague, and later for a Phd to Berlin, where I now live with my wife and our two kids. I like cycling, coffee, and chess. I work at Microsoft in AI for Science.
Publications
- Citation numbers (→) from Google Scholar
Research articles
| An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking · A. Foster, Z. Schätzle, P. B. Szabó, L. Cheng, J. Köhler, G. Cassella, N. Gao, J. Li, F. Noé & JH · Preprint at arXiv:2506.19960 (2025) | 11 |
| Accurate and scalable exchange-correlation with deep learning · G. Luise et al. · Preprint at arXiv:2506.14665 (2025) | 7 |
| Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space · S. Ehlert et al. · Preprint at arXiv:2506.14492 (2025) | 1 |
| Chemical Space Exploration with Artificial “Mindless” Molecules · T. Gasevic, M. Müller, J. Schöps, S. Lanius, JH, S. Grimme & A. Hansen · Preprint at doi:10/pt7m (2025) | 1 |
| Roadmap on Advancements of the FHI-aims Software Package · J. W. Abbott et al. · Preprint at arXiv:2505.00125 (2025) | 8 |
| Highly accurate real-space electron densities with neural networks · L. Cheng, P. B. Szabó, Z. Schätzle, D. P. Kooi, J. Köhler, K. J. H. Giesbertz, F. Noé, JH, P. Gori-Giorgi & A. Foster · J. Chem. Phys. 162, 034120 (2025) | 12 |
| Variational principle to regularize machine-learned density functionals: The non-interacting kinetic-energy functional · P. del Mazo-Sevillano & JH · J. Chem. Phys. 159, 194107 (2023) | 13 |
| libMBD: A general-purpose package for scalable quantum many-body dispersion calculations · JH, M. Stöhr, S. Góger, S. Chaudhuri, B. Aradi, R. J. Maurer & A. Tkatchenko · J. Chem. Phys. 159, 174802 (2023) | 22 |
| DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions · Z. Schätzle, P. B. Szabó, M. Mezera, JH & F. Noé · J. Chem. Phys. 159, 094108 (2023) | 32 |
| Ab initio quantum chemistry with neural-network wavefunctions · JH, J. Spencer, K. Choo, A. Mezzacapo, W. M. C. Foulkes, D. Pfau, G. Carleo & F. Noé · Nat. Rev. Chem. 7, 692–709 (2023) | 159 |
| Electronic excited states in deep variational Monte Carlo · M. T. Entwistle, Z. Schätzle, P. A. Erdman, JH & F. Noé · Nat. Commun. 14, 274 (2023) | 73 |
| Roadmap on Machine learning in electronic structure · H. J. Kulik et al. · Electron. Struct. 4, 023004 (2022) | 192 |
| Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEɴɢɪɴᴇ): Automation and interoperability among computational chemistry programs · D. G. A. Smith et al. · J. Chem. Phys. 155, 204801 (2021) | 54 |
| Anisotropic interlayer force field for transition metal dichalcogenides: The case of molybdenum disulfide · W. Ouyang, R. Sofer, X. Gao, JH, A. Tkatchenko, L. Kronik, M. Urbakh & O. Hod · J. Chem. Theory Comput. 17, 7237–7245 (2021) | 42 |
| Convergence to the fixed-node limit in deep variational Monte Carlo · Z. Schätzle, JH & F. Noé · J. Chem. Phys. 154, 124108 (2021) | 30 |
| Coulomb interactions between dipolar quantum fluctuations in van der Waals bound molecules and materials · M. Stöhr, M. Sadhukhan, Y. S. Al-Hamdani, JH & A. Tkatchenko · Nat. Commun. 12, 137 (2021) | 43 |
| Deep-neural-network solution of the electronic Schrödinger equation · JH, Z. Schätzle & F. Noé · Nat. Chem. 12, 891–897 (2020) | 738 |
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Fluctuational electrodynamics in atomic and macroscopic systems: van der Waals interactions and radiative heat transfer · P. S. Venkataram, JH, A. Tkatchenko & A. W. Rodriguez · Phys. Rev. B 102, 085403 (2020) * *Copyright 2020 by the American Physical Society |
4 |
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Recent developments in the PʏSCF program package · Q. Sun et al. · J. Chem. Phys. 153, 024109 (2020) * *This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. |
1242 |
| Density functional model for van der Waals interactions: Unifying many-body atomic approaches with nonlocal functionals · JH & A. Tkatchenko · Phys. Rev. Lett. 124, 146401 (2020) | 164 |
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DFTB+, a software package for efficient approximate density functional theory based atomistic simulations · B. Hourahine et al. · J. Chem. Phys. 152, 124101 (2020) * *This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. |
1200 |
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Nonlocal electronic correlations in the cohesive properties of high-pressure hydrogen solids · T. Cui, J. Li, W. Gao, JH, A. Tkatchenko & Q. Jiang · J. Phys. Chem. Lett. 11, 1521–1527 (2020) * *This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in The Journal of Physical Chemistry Letters, copyright © American Chemical Society after peer review. To access the final edited and published work follow this link. |
10 |
| Impact of nuclear vibrations on van der Waals and Casimir interactions at zero and finite temperature · P. S. Venkataram, JH, T. J. Vongkovit, A. Tkatchenko & A. W. Rodriguez · Sci. Adv. 5, eaaw0456 (2019) | 8 |
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Phonon-polariton mediated thermal radiation and heat transfer among molecules and macroscopic bodies: Nonlocal electromagnetic response at mesoscopic scales · P. S. Venkataram, JH, A. Tkatchenko & A. W. Rodriguez · Phys. Rev. Lett. 121, 045901 (2018) * *Copyright 2018 by the American Physical Society |
18 |
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Electronic exchange and correlation in van der Waals systems: Balancing semilocal and nonlocal energy contributions · JH & A. Tkatchenko · J. Chem. Theory Comput. 14, 1361–1369 (2018) * *This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Journal of Chemical Theory and Computation, copyright © American Chemical Society after peer review. To access the final edited and published work follow this link. |
40 |
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Unifying microscopic and continuum treatments of van der Waals and Casimir interactions · P. S. Venkataram, JH, A. Tkatchenko & A. W. Rodriguez · Phys. Rev. Lett. 118, 266802 (2017) * *Copyright 2017 by the American Physical Society |
33 |
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Tuning intermolecular interactions with nanostructured environments · M. Chattopadhyaya, JH, I. Poltavsky & A. Tkatchenko · Chem. Mater. 29, 2452–2458 (2017) * *This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Chemistry of Materials, copyright © American Chemical Society after peer review. To access the final edited and published work follow this link. |
12 |
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First-principles models for van der Waals interactions in molecules and materials: Concepts, theory, and applications · JH, R. A. DiStasio, Jr. & A. Tkatchenko · Chem. Rev. 117, 4714–4758 (2017) * *This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in Chemical Reviews, copyright © American Chemical Society after peer review. To access the final edited and published work follow this link. |
707 |
| Nanoscale π–π stacked molecules are bound by collective charge fluctuations · JH, D. Alfè & A. Tkatchenko · Nat. Commun. 8, 14052 (2017) | 113 |
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Communication: Many-body stabilization of non-covalent interactions: Structure, stability, and mechanics of Ag₃Co(CN)₆ framework · X. Liu, JH & A. Tkatchenko · J. Chem. Phys. 145, 241101 (2016) * *This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. |
19 |
| Theoretical investigation of layered zeolite frameworks: Surface properties of 2D zeolites · JH, M. Trachta, P. Nachtigall & O. Bludský · Catal. Today 227, 2–8 (2014) | 27 |
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A novel correction scheme for DFT: A combined vdW-DF/CCSD(T) approach · JH & O. Bludský · J. Chem. Phys. 139, 034115 (2013) * *This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. |
20 |
| Theoretical investigation of the Friedländer reaction catalysed by CuBTC: Concerted effect of the adjacent Cu²⁺ sites · M. Položij, E. Pérez-Mayoral, J. Čejka, JH & P. Nachtigall · Catal. Today 204, 101–107 (2013) | 38 |
Book chapters
| Introduction to material modeling · JH · In Machine learning meets quantum physics (eds K. T. Schütt et al.) 7–24 (Springer, 2020) | |
| Van der Waals interactions in material modelling · JH & A. Tkatchenko · In Handbook of materials modeling (eds W. Andreoni & S. Yip) 1–33 (Springer, 2018) | 4 |
Theses
| Towards unified density-functional model of van der Waals interactions · JH · Humboldt University (2018) | 7 |
| Nonlocal correlation in density functional theory · JH · Charles University (2013) |
Software
- DeepQMC · creator · 406 Deep learning quantum Monte Carlo for electrons in real space (Python)
- libMBD · creator · 57 Many-body dispersion library (Fortran)
- Pyberny · creator · 128 Molecular structure optimizer (Python)
- FHI-aims · core contributor All-electron electronic-structure calculations (Fortran)
- PySCF · contributor
- DFTB+ · contributor
- QCEngine · contributor
Presentations
Invited conference talks
| 2024 | “Neural-network wave functions for quantum chemistry” · European Seminar on Computational Methods in Quantum Chemistry (Copenhagen, Denmark) |
| 2023 | “Solving the electronic Schrödinger equation with deep learning” · SIAM Conference on Computational Science and Engineering (Amsterdam, Netherlands) |
| 2022 | “Libmbd: A general-purpose package for scalable many-body dispersion calculations” · Electronic Structure Software Development (Lausane, Switzerland) [virtual] |
| “Neural-network wave functions for quantum chemistry” · MLQC4DYN (Institut Pascal, Paris, France) | |
| “Neural-network wave functions for quantum chemistry” · Monte Carlo and Machine Learning Approaches in Quantum Mechanics (IPAM, Los Angeles, USA) | |
| 2021 | “Deep-learning solution to the electronic many-body problem” · Non-Covalent Interactions in Large Molecules and Extended Materials (EPFL, Lausanne, Switzerland) |
| “Solving the electronic Schrödinger equation with deep learning” · ACS Fall Meeting [virtual] | |
| 2020 | “Density-functional model for van der Waals interactions: Unifying atomic approaches with nonlocal functionals” · Electronic Structure Theory with Numeric Atom-Centered Basis Functions [virtual] |
| 2019 | “Unifying density-functional and interatomic approaches to van der Waals interactions” · Frontiers in Density Functional Theory and Beyond (Kavli ITS, Beijing, China) |
| 2018 | “Modeling van der Waals interactions in molecules and materials” · Molecular Simulations Meets Machine Learning and Artificial Intelligence (Lorentz Center, Leiden, Netherlands) |
| “Modeling van der Waals interactions in materials with many-body dispersion” · Electronic Structure Theory with Numeric Atom-Centered Basis Functions (TU Munich, Germany) | |
| “Modeling van der Waals interactions” · Python for Quantum Chemistry and Materials Simulation Software (Caltech, Pasadena, USA) |
Contributed conference talks
| 2021 | “Approaching exact solutions of the electronic Schrödinger equation with deep quantum Monte Carlo” · APS March Meeting [virtual] |
| 2020 | “Deep neural network solution of the electronic Schrödinger equation” · APS March Meeting (Denver, USA) [cancelled] |
| 2018 | “Unified many-body approach to van der Waals interactions based on semilocal polarizability functional” · APS March Meeting (Los Angeles, USA) |
| 2017 | “What is the range of electron correlation in density functionals?” · APS March Meeting (New Orleans, USA) |
| 2016 | “First-principles approaches to van der Waals interactions” · Many-Body Interactions (Telluride, USA) |
| 2015 | “Many-body dispersion meets non-local density functionals” · Modeling Many-Body Interactions (Lake La Garda, Italy) |
| “Many-body dispersion meets non-local density functionals” · DPG March Meeting (Berlin, Germany) | |
| “Many-body dispersion meets non-local density functionals” · APS March Meeting (San Antonio, USA) | |
| 2014 | “Non-local density functionals meet many-body dispersion” · DPG March Meeting (Dresden, Germany) |
| 2013 | “Adsorption in zeolites investigated by dispersion-corrected DFT” · Layered Materials (Liblice, Czechia) |
| “Modeling of surface properties of lamellar zeolites” · Molecular Sieves (Heyrovsky Institute, Prague, Czechia) |
Conference poster presentations
| 2021 | “Solving the electronic Schrödinger equation with deep learning” · Stochastic Methods in Electronic Structure Theory [virtual] |
| 2020 | “Convergence to the fixed-node limit in deep variational Monte Carlo” · NeurIPS workshop Machine Learning and the Physical Sciences [virtual] |
| 2019 | “Deep neural network solution of the electronic Schrödinger equation” · NeurIPS workshop Machine Learning and the Physical Sciences (Vancouver, Canada) |
| 2017 | “Balancing semilocal and nonlocal energy contributions in van der Waals systems” · Intermolecular Interactions (Arenas de Cabrales, Spain) |
| 2016 | “Python interface to FHI-aims” · Electronic Structure Theory with Numeric Atom-Centered Basis Functions (Munich, Germany) |
| 2015 | “Non-local density functionals meet many-body dispersion” · Psi-k Conference (San Sebastian, Spain) |
| “Many-body dispersion meets non-local density functionals” · Congress of Theoretical Chemists (Torino, Italy) | |
| “Non-local density functionals meet many-body dispersion” · Frontiers of First-Principles Simulations: Materials Design and Discovery (Berlin, Germany) | |
| 2014 | “Non-local density functionals meet many-body dispersion” · Addressing Challenges for First-Principles Based Modeling of Molecular Materials (Lausanne, Switzerland) |
| 2013 | “Modeling of surface properties of lamellar zeolites” · Molecular Sieves and Catalysis (Segovia, Spain) |
| 2012 | “Silver clusters in zeolites: Structure, stability and photoactivity” · British Zeolite Association Meeting (Chester, UK) |
| “Silver clusters in faujasite: A theoretical investigation” · Molecular Sieves (Prague, Czechia) |
Invited seminars
| 2022 | UCT & IOCB Theoretical Chemistry Seminar (VŠCHT, Prague, Czechia) |
| Lennard-Jones Centre Discussion Group (University of Cambridge) [virtual] | |
| 2021 | Molecular and Ultrafast Science Seminar (Center for Free-Electron Laser Science) [virtual] |
| Machine Learning seminar (Chalmers University of Technology) [virtual] | |
| Grüneis group seminar (TU Wien) [virtual] | |
| (Nano)Materials Modeling Seminar (Charles University) [virtual] | |
| Cosmology Seminar (University of Szczecin) [virtual] | |
| 2020 | “Solving the electronic Schrödinger equation with deep learning” · Scientific Machine Learning Mini-Course (Carnegie Mellon University) [virtual] |
| Machine Learning in Physics, Chemistry and Materials (University of Cambridge) [virtual] | |
| Jordan group seminar (University of Pittsburgh) [virtual] | |
| 2018 | “Mona: Calculation framework for reproducible science” · Theory Department seminar (Fritz Haber Institute, Berlin, Germany) |
| 2016 | “Nanoscale π–π stacked molecules bound by collective charge fluctuations” · Aspuru-Guzik group seminar (Harvard University, Cambridge, USA) |
| 2015 | DiStasio group seminar (Cornell University, Ithaca, USA) |
Employment
| Microsoft, Berlin | |
| Nov 2022– | Principal research manager · AI for Science |
| Free University of Berlin | |
| Nov 2020–Oct 2022 | Junior research group leader · Department of Mathematics |
| Jan 2019–Oct 2020 | Postdoctoral researcher · AI4Science group |
| University of Luxembourg | |
| Jan–Dec 2018 | Postdoctoral researcher · Theoretical Chemical Physics group |
| Fritz Haber Institute, Berlin | |
| Oct 2013–Dec 2017 | Graduate research assistant · Theory department |
| Institute of Organic Chemistry and Biochemistry, Prague | |
| Mar 2010–Sep 2013 | Undergraduate research assistant · Non-Covalent Interactions group |
Education
| Humboldt University of Berlin | |
| Dec 2017 | Ph.D. in Physics · summa cum laude |
| Charles University, Prague | |
| Sep 2013 | M.S. in Molecular Modeling |
| Sep 2011 | B.S. in Physics |
| Jun 2011 | B.S. in Chemistry |
Secondary appointments
| Jul 2021–Oct 2022 | Junior Fellow · BIFOLD, Berlin |
| Jan 2019–Oct 2020 | Postdoctoral research fellow · Machine Learning group, TU Berlin |
| Sep–Dec 2016 |
Visiting graduate researcher · IPAM, UCLA
(long program “Understanding Many-Particle Systems with Machine Learning”) |
Awards
| Feb 2021 | Marie Skłodowska-Curie Individual Fellowship [relinquished] |
| Jan 2014 | Heyrovsky Prize for the best science graduate · Charles University |
| Jul 2008 | Gold Medal · 39th International Physics Olympiad |
Professional activities
- Peer-reviewed 43 manuscripts for Phys. Rev. X, Nat. Commun., Nat. Mach. Intell., Phys. Rev. Lett., J. Chem. Phys., and other journals
- Reviewed 1 grant proposal for U. S. Department of Energy
Teaching & mentoring
Professional mentorship
| Sep 2022–Jul 2024 | U. C. Kaya, Master student |
| Mar–Sep 2022 | E. Trushin, Postdoc (with F. Noé) |
| Sep 2021–Oct 2022 | B. Szabó, Phd student (with F. Noé) |
| May 2021–Dec 2022 | P. del Mazo, Postdoc |
| Apr 2021–Apr 2022 | M. Höfler, Master student |
| Jul 2019–Jul 2020 | J. Lederer, Phd student, TU Berlin (with K.-R. Müller) |
| Jan 2019–Oct 2022 | Z. Schätzle, Master/Phd student (with F. Noé) |
Lectures for students
| 2022 | “Machine Learning in Quantum Chemistry” · IMPRS Summer School (Berlin, Germany) |
| “Basic principles of application of machine learning in quantum chemistry” (VŠCHT, Prague) | |
| 2019 | “Message-passing neural networks for modeling many-particle systems” · CECAM Summer School (Mainz, Germany) |
Doctoral committees
| 2022–2024 | B. Ames, University of Luxembourg |
| 2021 | M. Wilson, University of Bristol, UK |
Public outreach
| Sep 2019 | Public lecture in the Six Minute Challenge series, Czech Center, Berlin |
| 2018 | Mentored a student in the LEAF program, accepted to University of Edinburgh |
| Sep 2008–Jun 2010 | Co-organized FYKOS, physics competition for high school students |