About Gunnar Schmitz

Profil

"Be yourself; everyone else is already taken." Oscar Wilde

I am a postdoctoral researcher at the Ruhr university Bochum at the Research Center for Chemical Science and Sustainability at the Ruhr university Bochum under the supervision of Prof. Jörg Behler. My research deals with the construction of Machine Learning potentials (MLPs) for use in Molecular Dynamics simulations. If trained on accurate quantum chemical data (DFT, Coupled Cluster) MLPs allow for very accurate simulations on long time scales. At the research center we mainly employ potential based on Neural Networks, but due to my expertise also potentials based on Gaussian Process Regression.

Prior to this position of I was a postdoctoral researcher at the Ruhr university Bochum in the groups of Prof. Rochus Schmid and Christof Hättig, where I work on the atomistic modeling of combustion reaction under oxyfuel conditions by means of molecular dynamic simulations. Pivotal for this task is the development of an automatic analysis of trajectories to identify elementary reactions to be closer investigated by accurate electronic structure methods like DFT or CC methods.

Before my position at the Ruhr university I worked at Aarhus university in the group of Prof. Ove Christiansen as postdoctoral researcher. I worked on using Machine Learning techniques to efficiently construct potential energy surfaces (PESs) for subsequent use in vibrational structure methods like vibrational Coupled Cluster (VCC). However, in the future I aim at using them in a more broad perspective. ML algorithms open the door to construct for example specialized force fields (FFs), which can achieve Coupled Cluster (CC) accuracy if properly trained. And their application is not restricted to FFs, but also to be used in thermo chemistry protocols and the prediction of various properties. Furthermore, a combination with evolutionary algorithms could be very fruitful for various optimization problems. A particular problem I have in mind is the construction of complementary basis sets for use in F12 Theory. The limited amount of these basis sets at the moment highly limits their usage.

I received my PhD in Theoretical Chemistry at the Ruhr-Universität Bochum in 2016 under supervision of Prof. Christof Hättig. During my PhD I worked on the development, implementation and application of low scaling explicitly correlated electronic structure methods using pair natural orbitals (PNOs) and F12 Theory. PNOs are a tensor factorization of the doubles amplitude space and pave the way for reduced scaling implementations. A reoccurring topic was the study of interactions in host guest system. We could establish new benchmark values for the famous S66 set and do studies in large systems as a Calix[4]arene water complex on the PNO-CCSD(F12*)(T) level.

Publications

  1. C.Hättig, G.Schmitz, J.Koßmann, "Auxiliary basis sets for density-fitted correlated wavefunction calculations: weighted core-valence and ECP basis sets for post-d elements", Phys. Chem. Chem. Phys. 2012,14,6549-6555.
  2. G.Schmitz, B.Helmich, C.Hättig, "A cubic scaling PNO–MP2 method using a hybrid OSV–PNO approach with an iterative direct generation of OSVs" Mol. Phys. 2013,111,2463-2476.
  3. G.Schmitz, C.Hättig, D.P.Tew, "Explicitly correlated PNO-MP2 and PNO-CCSD and their application to the S66 set and large molecular systems", Phys. Chem. Chem. Phys.,2014,16, 22167-22178.
  4. G.Schmitz, C.Hättig, "Perturbative triples correction for local pair natural orbital based explicitly correlated CCSD (F12*) using Laplace transformation techniques", The Journal of Chemical Physics,2016,145,234107.
  5. M.Frank, G.Schmitz, C.Hättig, "The PNO–MP2 gradient and its application to molecular geometry optimisations" ,Mol. Phys.,2017,115,343-356.
  6. G.Schmitz, N.K.Madsen, O.Christiansen, "Atomic-batched tensor decomposed two-electron repulsion integrals", The Journal of Chemical Physics,2017,146, 134112.
  7. G.Schmitz, C.Hättig, "Accuracy of explicitly correlated local PNO-CCSD (T)", Journal of Chemical Theory and Computation,2017,13, 2623-2633.
  8. G.Schmitz, O.Christiansen, "Accuracy of frequencies obtained with the aid of explicitly correlated wave function based methods", Journal of Chemical Theory and Computation,2017,13, 3602-3613.
  9. B.Fiedler, G.Schmitz, C.Hättig, "Combining Accuracy and Efficiency: An Incremental Focal-Point Method Based on Pair Natural Orbitals", J.Friederich, Journal of Chemical Theory and Computation,2017,13, 6023-6042.
  10. G.Schmitz, O.Christiansen, "Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation", The Journal of Chemical Physics,2018,148,241704
  11. G.Schmitz, O.Christiansen, "Assessment of the overlap metric in the context of RI-MP2 and atomic batched tensor decomposed MP2", Chemical Physics Letters,2018,701,7-14
  12. G.Schmitz, D.G.Artiukhin, O.Christiansen, "Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling", The Journal of Chemical Physics, 2019, 150 (13), 131102
  13. G.Schmitz, I.H.Godtliebsen, O.Christiansen, "Machine learning for potential energy surfaces: An extensive database and assessment of methods", The Journal of Chemical Physics, 2019, 150 (24), 244113
  14. G.Schmitz, J.Elm, "Assessment of the DLPNO Binding Energies of Strongly Noncovalent Bonded Atmospheric Molecular Clusters", ACS OMEGA, 2020
  15. Sree Ganesh Balasubramani, Guo P. Chen, Sonia Coriani, Michael Diedenhofen, Marius S. Frank, Yannick J. Franzke, Filipp Furche, Robin Grotjahn, Michael E. Harding, Christof Hättig, Arnim Hellweg, Benjamin Helmich-Paris, Christof Holzer, Uwe Huniar, Martin Kaupp, Alireza Marefat Khah, Sarah Karbalaei Khani, Thomas Müller, Fabian Mack, Brian D. Nguyen, Shane M. Parker, Eva Perlt, Dmitrij Rappoport, Kevin Reiter, Saswata Roy, Matthias Rückert, Gunnar Schmitz, Marek Sierka, Enrico Tapavicza, David P. Tew, Christoph van Wüllen, Vamsee K. Voora, Florian Weigend, Artur Wodyński, and Jason M. Yu, "TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations", The Journal of Chemical Physics, 2020, 152, 184107
  16. M.S.Frank, G.Schmitz, C.Hättig, "Implementation of the iterative triples model CC3 for excitation energies using pair natural orbitals and Laplace transformation techniques", The Journal of Chemical Physics, 2020, 153, 034109
  17. G.Schmitz,E.L.Klinting, O. Christiansen, "A Gaussian process regression adaptive density guided approach for potential energy surface construction",The Journal of Chemical Physics, 2020, 153, 064105
  18. Ö.Yönder, G.Schmitz, C.Hättig, R. Schmid, P. Debiagi, C. Hasse, A. Locaspi, T. Faravelli, "Can Small Polyaromatics Describe Their Larger Counterparts for Local Reactions? A Computational Study on the H-Abstraction Reaction by an H-Atom from Polyaromatics",The Journal of Physical Chemistry A, 2020, 124, 9626-9637
  19. G. Schmitz, Ö. Yönder, B. Schnieder, R. Schmid, C. Hättig, "An automatized workflow from molecular dynamic simulation to quantum chemical methods to identify elementary reactions and compute reaction constants", Journal of Computational Chemistry, 2021, 42, 2264-2282.
  20. G. Schmitz, B. Schnieder, "Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets", Journal of Computational Chemistry, 2022, NA, NA.
  21. A. Buchgraitz Jensen, J. Kubečkam, G. Schmitz, O. Christiansen, J. Elm, "Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters", Journal of Chemical Theory and Computation, 2022, 18 (12),7373-7383.
  22. C. Pflieger, T. Eckhard, G. Schmitz, V. Angenent, M. Göckeler, O. Senneca, R. Schmid, F. Cerciello, M. Muhler, "Thermicity of the Decomposition of Oxygen Functional Groups on Cellulose-Derived Chars",ACS Omega, 2022, 7, 51, 48606–48614.
  23. D. G. Artiukhin, I. H. Godtliebsen, G. Schmitz, O. Christiansen, "Gaussian process regression adaptive density-guided approach: Toward calculations of potential energy surfaces for larger molecules", J. Chem. Phys., 2023, 159, 024102.
  24. Ö. Yönder, G. Schmitz, R. Schmid, B. Schnieder, C. Hättig, "A computational chemistry study on the evolution of oxygen functional groups during char burnout", Fuel, 2024, 365, 131217.



Talks



Poster

social