Gunnar Schmitz
Affiliations: | 2013-2016 | Chemistry and Biochemistry | Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany |
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| Artiukhin DG, Godtliebsen IH, Schmitz G, et al. (2023) Gaussian process regression adaptive density-guided approach: Toward calculations of potential energy surfaces for larger molecules. The Journal of Chemical Physics. 159 |
| Jensen AB, Kubečka J, Schmitz G, et al. (2022) Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters. Journal of Chemical Theory and Computation. 18: 7373-7383 |
| Schmitz G, Klinting EL, Christiansen O. (2022) A Gaussian process regression adaptive density guided approach for potential energy surface construction. The Journal of Chemical Physics. 153: 064105 |
| Schmitz G, Yönder Ö, Schnieder B, et al. (2021) An automatized workflow from molecular dynamic simulation to quantum chemical methods to identify elementary reactions and compute reaction constants. Journal of Computational Chemistry. 42: 2264-2282 |
| Yönder Ö, Schmitz G, Hättig C, et al. (2020) 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. 124: 9626-9637 |
| Frank MS, Schmitz G, Hättig C. (2020) Implementation of the iterative triples model CC3 for excitation energies using pair natural orbitals and Laplace transformation techniques. The Journal of Chemical Physics. 153: 034109 |
| Balasubramani SG, Chen GP, Coriani S, et al. (2020) TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations. The Journal of Chemical Physics. 152: 184107 |
| Schmitz G, Elm J. (2020) Assessment of the DLPNO Binding Energies of Strongly Noncovalent Bonded Atmospheric Molecular Clusters. Acs Omega. 5: 7601-7612 |
| Schmitz G, Godtliebsen IH, Christiansen O. (2019) Machine learning for potential energy surfaces: An extensive database and assessment of methods. The Journal of Chemical Physics. 150: 244113 |
| Schmitz G, Artiukhin DG, Christiansen O. (2019) Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling. The Journal of Chemical Physics. 150: 131102 |