31. Predicting the stability of base-mediated C–H carboxylation adducts using data science tools
M. Eckhoff, A. Garcia-Roca, S. Deolka, L. Seidel, M. S. Sigman, J. Proppe, https://doi.org/10.26434/chemrxiv-2025-5tlrl
30. regAL: Python Package for Active Learning of Regression Problems
E. Surzhikova, J. Proppe, https://arxiv.org/abs/2410.17917
29. Unveiling CO2 reactivity with data-driven methods
M. Eckhoff, K. L. Bublitz, J. Proppe, Digit. Discov. 2025, 4, 868
28. Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond
K. Janssen, J. Proppe, J. Chem. Inf. Model. 2025, 65, 1862
27. Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles
K. Janssen, J. Kirchmair, J. Proppe, ChemMedChem 2024, 19, e202400307
26. Myoglobin-catalyzed azide reduction proceeds via an anionic metal amide intermediate
M. Tinzl, J. V. Diedrich, P. Mittl, M. Clémancey, M. Reiher, J. Proppe, J.-M. Latour, D. Hilvert, J. Am. Chem. Soc. 2024, 146, 1957
25. Quantitative structure–reactivity relationships for synthesis planning: The benzhydrylium case
M. Eckhoff, J. V. Diedrich, M. Mücke, J. Proppe, J. Phys. Chem. A 2024, 128, 343
24. The Computational Road to Reactivity Scales
M. Vahl, J. Proppe, Phys. Chem. Chem. Phys. 2023, 25, 2717
23. Learning Conductance: Gaussian Process Regression for Molecular Electronics
M. Deffner, M. P. Weise, H. Zhang, M. Mücke, J. Proppe, I. Franco, C. Herrmann, J. Chem. Theory Comput. 2023, 19, 992
22. Uncertainty Quantification of Reactivity Scales
J. Proppe, J. Kircher, ChemPhysChem 2022, 23, e202200061
21. Machine-Learned Potentials for Next-Generation Matter Simulations
P. Friederich, F. Häse, J. Proppe, A. Aspuru-Guzik, Nat. Mater. 2021, 20, 750
20. Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value-added Products
M. Dörr, M. M. Hielscher, J. Proppe, S. R. Waldvogel, ChemElectroChem 2021, 8, 2621
19. Theoretical Studies of the Acid–Base Equilibria in a Model Active Site of the Human 20S Proteasome
J. Uranga, L. Hasecke, J. Proppe, J. Fingerhut, R. A. Mata, J. Chem. Inf. Model. 2021, 61, 1942
18. Calibration of Computational Mössbauer Spectroscopy to Unravel Active Sites in FeNC-Catalysts for the Oxygen Reduction Reaction
C. Gallenkamp, U. I. Kramm, J. Proppe, V. Krewald, Int. J. Quantum Chem. 2021, 121, e26394
17. Exchange Spin Coupling from Gaussian Process Regression
M. P. Bahlke, N. Mogos, J. Proppe, C. Herrmann, J. Phys. Chem. A 2020, 124, 8708
16. A Multi-Label, Dual-Output Deep Neural Network for Automated Bug Triaging
C. A. Choquette-Choo, D. Sheldon, J. Proppe, J. Alphonso-Gibbs, H. Gupta, 18th International Conference on Machine Learning Applications (2019), DOI: 10.1109/ICMLA.2019.00161
15. Gaussian Process-Based Refinement of Dispersion Corrections
J. Proppe, S. Gugler, M. Reiher, J. Chem. Theory Comput. 2019, 15, 6046
14. Mechanism Deduction from Noisy Chemical Reaction Networks
J. Proppe, M. Reiher, J. Chem. Theory Comput. 2019, 15, 357
13. Computational Systems Chemistry with Rigorous Uncertainty Quantification
J. Proppe, Dissertation, ETH Zürich, Schweiz (2018)
12. Capture and Characterization of a Reactive Haem–Carbenoid Complex in an Artificial Metalloenzyme
T. Hayashi, M. Tinzl, T. Mori, U. Krengel, J. Proppe, J. Soetbeer, D. Klose, G. Jeschke, M. Reiher, D. Hilvert, Nat. Catal. 2018, 1, 578
11. Statistical Analysis of Semiclassical Dispersion Corrections
T. Weymuth, J. Proppe, M. Reiher, J. Chem. Theory Comput. 2018, 14, 2480
10. Reliable Estimation of Prediction Uncertainty for Physicochemical Property Models
J. Proppe, M. Reiher, J. Them. Theory Comput. 2017, 13, 3297
9. Error Assessment of Computational Models in Chemistry
G. N. Simm, J. Proppe, M. Reiher, Chimia 2017, 71, 202
8. Reaction Rate Theory — New Methods: General Discussion
G. Angulo, R. D. Astumian, V. Beniwal, P. G. Bolhuis, C. Dellago, J. Ellis, B. Ensing, D. R. Glowacki, S. Hammes-Schiffer, J. Kästner, T. Lelièvre, N. Makri, D. Manolopoulos, G. Menzl, T. F. Miller, A. Mulholland, E. A. Oprzeska-Zingrebe, M. Parrinello, E. Pollak, J. Proppe, M. Reiher, J. Richardson, P. R. Chowdhury, E. Sanz, C. Schütte, D. Shalashilin, R. Szabla, S. Taraphder, A. Tiwari, E. Vanden-Eijnden, A. Vijaykumar, K. Zinovjev, Faraday Discuss. 2016, 195, 521
7. Uncertainty Quantification for Quantum Chemical Models of Complex Reaction Networks
J. Proppe, T. Husch, G. N. Simm, M. Reiher, Faraday Discuss. 2016, 195, 497
6. Heuristics-Guided Exploration of Reaction Mechanisms
M. Bergeler, G. N. Simm, J. Proppe, M. Reiher, J. Chem. Theory Comput. 2015, 11, 5712
5. An Extended Flory Distribution for Kinetically Controlled Step-Growth Polymerizations Perturbed by Intramolecular Reactions
J. Proppe, Macromol. Theory Simul. 2015, 24, 500
4. Communication Through Molecular Bridges: Different Bridge Orbital Trends Result in Common Property Trends
J. Proppe, C. Herrmann, J. Comput. Chem. 2015, 36, 201
3. Charge Delocalization in an Organic Mixed Valent Bithiophene is Greater Than in a Structurally Analogous Biselenophene
A. C. Jahnke, J. Proppe, M. Spulber, C. G. Palivan, C. Herrmann, O. S. Wenger, J. Phys. Chem. A 2014, 118, 11293
2. A Refined Flory Distribution for Step-Growth Polymerizations Comprising Cyclic Molecules
J. Proppe, G. A. Luinstra, Macromol. Theory Simul. 2013, 22, DOI:10.1002/mats.201300117
*The authors' opinions differ, cf. Ref. 21 in J. Proppe, Macromol. Theory Simul. 2015, 24, 500.
1. Graphite Nanoplatelet/Pyromellitic Dianhydride Melt Modified PPC Composites: Preparation and Characterization
C. Barreto, J. Proppe, S. Frederiksen, E. Hansen, R. W. Rychwalski, Polymer 2013, 54, 3574