 | Dr. Johannes Dietschreit University of Vienna Institute of Theoretical Chemistry Währinger Str. 17 1090 Vienna Austria johannes.dietschreit@univie.ac.at +43 1 4277 52764 ORCID: 0000-0002-5840-0002 |
| Focus Area |
| My research combines statistical thermodynamics and machine learning. My aim is to apply modern machine learning algorithms and sampling to photochemical reactions. In the past I have used enhanced sampling to simulate chemical reactions and to construct meaningful data sets when training interatomic potentials. We made it possible to use differentiable simulations to learn on the fly from molecular dynamics simulations. Additionally, I derived rigorous expressions to compute activation and reaction free energies, internal energy and entropies as well as their coordinate dependent profiles. |
| Awards & Prizes |
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| Publications |
Revisiting the Intricate Photodissociation Mechanism of Ammonia along the Minor NH + H2 Pathway (submitted), (2025) 24. A. R. Tan, J. Dietschreit*, R. Gómez-Bombarelli Enhanced Sampling of Robust Molecular Datasets with Uncertainty-based Collective Variables J. Chem. Phys. 162, 034114, (2025), DOI: 10.1063/5.0246178 23. S. Yang, J. Nam, J. Dietschreit*, R. Gómez-Bombarelli Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation J. Chem. Theory Comput. 20, 6559–6568, (2024), DOI: 10.1021/acs.jctc.4c00435 22. M. C. Pöverlein, A. Hulm, J. Dietschreit*, J. Kussmann, C. Ochsenfeld, V. R. I. Kaila QM/MM Free Energy Calculations of Long-Range Biological Protonation Dynamics by Adaptive and Focused Sampling J. Chem. Theory Comput. 20, 5751-5762, (2024), DOI: 10.1021/acs.jctc.4c00199 21. J. K. Szántó, J. Dietschreit*, M. Shein, A. K. Schütz, C. Ochsenfeld Systematic QM/MM Study for Predicting 31P NMR Chemical Shifts of Adenosine Nucleotides in Solution and Stages of ATP Hydrolysis in a Protein Environment J. Chem. Theory Comput. 20, 2433–2444, (2024), DOI: 10.1021/acs.jctc.3c01280 20. A. R. Tan, S. Urata, S. Goldman, J. Dietschreit*, R. Gómez-Bombarelli Single-model Uncertainty Quantification in Neural Network Potentials does not Consistently Outperform Model Ensembles npj Comp. Mat. 9, 225, (2023), DOI: 10.1038/s41524-023-01180-8 19. J. Dietschreit*, J. D. Diestler, R. Gómez-Bombarelli Entropy and Energy Profiles of Chemical Reactions J. Chem. Theory Comput. 19, 5369-5379, (2023), DOI: 10.1021/acs.jctc.3c00448 18. M. Šípka, J. Dietschreit*, L. Grajciar, R. Gómez-Bombarelli Differentiable Simulations for Enhanced Sampling of Rare Events Proc. 40th Int. Conf. ML PMLR , 202:31990-32007, (2023) 17. W. Wang, Z. Wu, J. Dietschreit*, R. Gómez-Bombarelli Learning Pair Potentials using Differentiable Simulations J. Chem. Phys. 158, 044113, (2023), DOI: 10.1063/5.0126475 16. H. Laqua, J. Dietschreit*, J. Kussman, C. Ochsenfeld Accelerating Hybrid Density Functional Theory Molecular Dynamic Simulations by Seminumerical Integration, Resolution- of-the-Identity Approximation, and Graphics Processing Units J. Chem. Theory Comput. 18, 6010-6020, (2022), DOI: 10.1021/acs.jctc.2c00509 15. C. Glas, E. Neydenova, S. Lechner, N. Wössner, L. Yang, J. Dietschreit*, H. Sun, M. Jung, B. Kuster, C. Ochsenfeld, F. Bracher Development of Hetero-triaryls as a new Chemotype for Subtype-selective and Potent Sirt5 Inhibition Eur. J. Med. Chem. 240, 114594, (2022), DOI: 10.1016/j.ejmech.2022.114594 14. J. Dietschreit*, D. J. Diestler, A. Hulm, C. Ochsenfeld, R. Gómez-Bombarelli From Free-Energy Profiles to Activation Free Energies J. Chem. Phys. 157, 084113, (2022), DOI: 10.1063/5.0102075 13. A. Hulm, J. Dietschreit*, C. Ochsenfeld Statistically Optimal Analysis of the Extended-system Adaptive Biasing Force (eABF) Method J. Chem. Phys. 157, 024110, (2022), DOI: 10.1063/5.0095554 12. J. Dietschreit*, D. J. Diestler, C. Ochsenfeld How to Obtain Reaction Free Energies from Eree-energy Profiles J. Chem. Phys. 156, 114105, (2022), DOI: 10.1063/5.0083423 11. J. Dietschreit*, B. von der Esch, C. Ochsenfeld Exponential Averaging versus Umbrella Sampling for Computing the QM/MM Free Energy Barrier of the Initial Step of the Desuccinylation Reaction Catalyzed by Sirtuin 5 Phys. Chem. Chem. Phys. 24, 7723-7731, (2022), DOI: 10.1039/D1CP05007A 10. C. Glas, J. Dietschreit*, N. Wössner, L. Urban, E. Ghazy, W. Sippl, M. Jung, C. Ochsenfeld, F. Bracher Identification of the Subtype-selective Sirt5 Inhibitor Balsalazide through Systematic SAR Analysis and Rationalization via Theoretical Investigations Eur. J. Med. Chem. 20, 112676, (2020), DOI: 10.1016/j.ejmech.2020.112676 9. J. Dietschreit*, A. Wagner, T. A. Le, P. Klein, H. Schindelin, T. Opatz, B. Engels, U. A. Hellmich, C. Ochsenfeld Predicting 19F NMR Chemical Shifts: A Combined Computational and Experimental Study of a Trypanosomal Oxidoreductase-Inhibitor Complex Angew. Chem. Int. Ed. 59, 12669-12673, (2020), DOI: 10.1002/anie.202000539 8. S. Vogler, J. Dietschreit*, L. D. M. Peters, C. Ochsenfeld Important Components for Accurate Hyperfine Coupling Constants: Electron Correlation, Dynamic Contributions, and Solvation Effects Mol. Phys. , e1772515, (2020), DOI: 10.1080/00268976.2020.1772515 7. J. Egli, T. Schnitzer, J. Dietschreit*, C. Ochsenfeld, H. Wennemers Why Proline? Influence of Ring-Size on the Collagen Triple Helix Org. Lett. 22, 348-351, (2020), DOI: 10.1021/acs.orglett.9b03528 6. B. von der Esch, J. Dietschreit*, L. D. M. Peters, C. Ochsenfeld Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5 J. Chem. Theory Comput. 15, 6660-6667, (2019), DOI: 10.1021/acs.jctc.9b00876 5. L. D. M. Peters, J. Dietschreit*, J. Kussmann, C. Ochsenfeld Calculating Free Energies from the Vibrational Density of States Function: Validation and Critical Assessment J. Chem. Phys. 150, 194111, (2019), DOI: 10.1063/1.5079643 4. E. Naydenova, J. Dietschreit*, C. Ochsenfeld Reaction Mechanism for the N-Glycosidic Bond Cleavage of 5-Formylcytosine by Thymine DNA Glycosylase J. Phys. Chem. B 123, 4173-4179, (2019), DOI: 10.1021/acs.jpcb.8b11706 3. J. Dietschreit*, L. D. M. Peters, J. Kussmann, C. Ochsenfeld Identifying Free Energy Hot-Spots in Molecular Transformations J. Phys. Chem. 123, 2163–2170, (2019), DOI: 10.1021/acs.jpca.8b12309 2. J. Dietschreit*, D. J. Diestler, E. Knapp Chemically Realistic Tetrahedral Lattice Models for Polymer Chains: Application to Polyethylene Oxide. J. Chem. Theory Comput. 12, 2388–2400, (2016), DOI: 10.1021/acs.jctc.6b00144 1. J. Dietschreit*, D. J. Diestler, E. Knapp Models for Self-Avoiding Polymer Chains on the Tetrahedral Lattice Macromol. Theory Simul. 23, 452-463, (2014), DOI: 10.1002/mats.201400023 |