Publications
Preprints
69. Ultrafast light-induced formation of a metastable hidden state in bismuth vanadate,
V. F. Kunzelmann, V. Streibel, P. Schwinghammer, P. Kollenz, B. Guzelturk, F. S. Hegner, L. Eyre, F. P. Delgado, T. A. Zewdie, M. W. Heindl, D. D. Monteiro Galindo, D. Sandner, G. Zhou, E. Sirotti, S. Bodnar, Y. Jiang, Y. Uemura, T. Eklund, F. Lima, X. Huang, D. Vinci, F. Ardana Lamas, P. Zalden, H. Iglev, D. A. Egger, F. Deschler, I. D. Sharp. (2025, under review)
DOI: https://arxiv.org/abs/2512.08287
68. Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks, Zhu, X.; Rinke, P.; Egger, D. A. (2025, under review)
DOI: DOI.org/10.48550/arXiv.2511.18398
67. Thermodynamic potentials from a probabilistic view on the system-environment interaction energy, M. Rahbar, C. J. Stein (2025, under review)
DOI: DOI.org/10.48550/arXiv.2505.00188
66. A probabilistic approach to system-environment coupling, M. Rahbar, C. J. Stein (2025, under review)
DOI: DOI.org/10.48550/arXiv.2505.00192
65. Single-Atom Tuning of Structural and Optoelectronic Properties in Halogenated Anthracene-Based Covalent Organic Frameworks, K. Paliušytė, L. Fuchs, Z. Xu, K. Liu, K. Roztocki, S. Sun, H. Zipse, A. Hartschuh, F. Ortmann, J. Schneider (2026, preprint) arXiv:2601.12103
DOI: DOI.org/10.48550/arXiv.2601.12103
64. Organic Hydrogen Sensors for Potential Use in Safety-Critical Environments, A. Morgenstern, L. Viriato, F. Ortmann, C. Bickmann, L. Hertling, D. Weber, D. R. T. Zahn, K. Hiller, T. v. Unwerth, D. Schondelmaier, G. Salvan (2026, preprint) arXiv:2602.04434
DOI: DOI.org/10.48550/arXiv.2602.04434
63. Ultrafast Photo-Thermoelectric Currents in Graphene Junctions in the Mid-Infrared, N. Pettinger, M. Panhans, J. Schmuck, S. Loy, X. Zhou, C. Dong, J. A. Robinson, S. Zherebtsov, C. Kastl, F. Ortmann, A. W. Holleitner (2026, preprint) arXiv:2603.13457
DOI: DOI.org/10.48550/arXiv.2603.13457
62. Photocatalytic Eosin Y Disproportionation as Driving Force for Terpene Cyclization, T. Rittner, C. Lucht, N. Rascón, A. Abramov, L. Hetzel, C. J. Stein, R. Gschwind, P. Nuernberger, T. Gulder, ChemRxiv. (2025, under review)
DOI: DOI.org/10.26434/chemrxiv-2025-hx8h0
61. Exact fluctuation relation for open systems beyond the Jarzynski equality, M. Rahbar, C. J. Stein, arXiv:2511.10236 (2025, under review).
DOI: DOI.org/10.48550/arXiv.2511.10236
60. Exact fluctuation relation for open systems beyond the Zwanzig FEP equation, M. Rahbar, C. J. Stein, arXiv:2512.11570 (2025, under review)
DOI: DOI.org/10.48550/arXiv.2512.11570
59. Investigating the Electrochemical Double Layer with Quantum-Chemical Simulations and Implicit Solvation Models, A. Mangiameli, C. J. Stein, arXiv:2603.29674 (2026, under review)
DOI: DOI.org/10.48550/arXiv.2603.29674
58. Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling, N. Costa, J. Zavadlav (2025, under review)
DOI:10.48550/arXiv.2509.02060
57. Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions, P. Fuchs, J. Zavadlav (2025, under review)
DOI:10.48550/arXiv.2512.03974v1
56. Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces, M. Sanocki, J. Zavadlav (2025, under review)
DOI:10.48550/arXiv.2512.10989
55. Coarse-Grained Boltzmann Generators, W. Chen, B. Zhao, J. Eckwert, J. Zavadlav (2026, under review)
DOI:10.48550/arXiv.2602.10637
54. Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations. Störmer, J. Zavadlav (2026, under review)
DOI:10.48550/arXiv.2603.24360
2026
53. TheMeCat: Dataset of Thermocatalytic Conversion of CO2 to Methanol, Á. Toldy, P. Pisal, O.j Krejčí, P. Rinke, A. Santasalo-Aarnio. Sci Data 13, 165 (2026).
DOI: DOI.org/10.1038/s41597-025-06482-8
52. Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations, Grumet, M.;Miyagawa, T.; Pittet, O.; Pegolo, P.; Thalmann, K. S.; Kaiser, W.; Egger, D. A. AI for Science, in press (2026)
DOI: DOI.org/10.1088/3050-287X/ae411a
51. An interpretable molecular descriptor for machine learning predictions in atmospheric science, L. Lind, H. Sandström, P. Rinke. J. Chem. Phys. 28 February 2026; 164 (8): 084115.
DOI: DOI.org/10.1063/5.0308548
50. GAP vs. MACE: efficiency evaluation in a liquid electrolyte system. A. Beiersdorfer, L. Hetzel, F. Deißenbeck, C. Staacke, C. J. Stein, Mach. Learn.: Sci. Technol. 7 015016. (2026)
DOI: DOI.org/10.1088/2632-2153/ae35cf
49. Extensions to Extended Tight-Binding Methods for Transition-Metal Containing Systems, S. Moradi, R. Tomann, M. Head-Gordon, C. J. Stein, J. Comput. Chem. 47, no. 7, e70346. (2026)
DOI: DOI.org/10.1002/jcc.70346
48. Short-range machine-learning potentials for aqueous electrolyte solutions, L. Hetzel, C. J. Stein, Chem. Phys. Chem., accepted. (2026)
DOI: 10.1002/cphc.70385
47. Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction, M. Schwade, S. Zhang, F. Vonhoff, F.P. Delgado, D.A. Egger, Nat Commun 17, 2652 (2026).
DOI: doi.org/10.1038/s41467-026-70865-7
46. Achieving All-Atom Molecular Dynamics Accuracy from the Poisson–Boltzmann Method Through Machine Learning, E. Slejko, A. Coste, T. Potisk, J. Zavadlav, M. Praprotnik (2026), The Journal of Chemical Physics 164 (5)
DOI: DOI.org/10.1063/5.0313624
45. Mapping Still Matters: Coarse-Graining with Machine Learning Potentials, F. Görlich, J. Zavadlav (2026), Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.5c03035
2025
44. Design rules for optimizing quaternary mixed-metal chalcohalides. P. Henkel, J. Li, P. Rinke. Phys. Rev. Materials 9, 115405, 2025.
DOI: DOI.org/10.1103/qjwt-29w9
43. Similarity-based analysis of atmospheric organic compounds for machine learning applications. H. Sandström, P. Rinke. Geosci. Model Dev., 18, 2701–2724, 2025.
DOI: DOI.org/10.5194/gmd-18-2701-2025
42. Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction. N. Bhatia, P. Rinke, O. Krejčí. npj Comput Mater 11, 324 (2025).
DOI: DOI.org/10.1038/s41524-025-01827-8
41. Traffic-Emitted Amines Promote New Particle Formation at Roadsides. J. Brean, F. Bortolussi, A. Rowell, D. C. S. Beddows, K. Weinhold, P. Mettke, M. Merkel, A. Kumar, S. Barua, S. Iyer, A. Karppinen, H. Sandström, P. Rinke, A. Wiedensohler, M. Pöhlker, M. Dal Maso, M. Rissanen, Z. Shi, R. M. Harrison. ACS ES&T Air 2025 2 (8), 1704-1713
DOI: DOI.org/10.1021/acsestair.5c00119
40. Competition Between One‐ and Two‐Electron Unimolecular Reactions of Late 3d‐Metal Complexes [(Me3SiCH2)nM]–, T. Kühl, L. Hetzel, C. J. Stein, K. Koszinowski, Angew. Chem. Int. Ed. 64, e202500524 (2025)
DOI: DOI.org/10.1002/anie.202500524
39. Unlocking high-performance photocapacitors for edge computing in low-light environments, N. Flores-Diaz et al., Energy Environ. Sci. 18(10), 4704–4716 (2025)
DOI: DOI.org/10.1039/d5ee01052g
38. Single-Layer MoS2-Based Atomristor’s Resistive Switching Model for SET Sweep with Density Functional Theory Simulations, A. Turfanda, A. Gagliardi, ACS Appl. Electron. Mater. 7, 3795–3809 (2025)
DOI: DOI.org/10.1021/acsaelm.5c00061
37. AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites, I. Kouroudis, Poonam, N. Misciasci, F. Mayr, L. Müller, Z. Gu, A. Gagliardi, npj Comput. Mater. 11(1), 1–13 (2025)
DOI: DOI.org/10.1038/s41524-025-01630-5
36. chemtrain: Learning deep potential models via automatic differentiation and statistical physics, Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav, Comput. Phys. Commun. 310, 109512 (2025)
DOI: dx.DOI.org/10.1016/j.cpc.2025.109512
35. Analysis of real-space transport channels for electrons and holes in halide perovskites Vonhoff, F.; Schilcher, M. J.; Reichman, D. R.; Egger, D. A. Phys. Rev. Mater. 9, 094601 (2025) (Selected as “Editor’s Suggestion”)
DOI:https://DOI.org/10.1103/c4v6-kf43
34. Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces, F. Delgado, F. Simões, L. Kronik, W. Kaiser, D. A. Egger, ACS Energy Lett. 10, 3367 (2025)
DOI: pubs.acs.org/DOI/10.1021/acsenergylett.5c01519
33. Effect of Overdamped Phonons on the Fundamental Band Gap of Perovskites, X. Zhu, D. A. Egger, Phys. Rev. Lett. 134, 016403 (2025)
DOI: DOI.org/10.1103/PhysRevLett.134.016403
32. Enhancing Lignin-Carbohydrate Complexes Production and Properties with Machine Learning, D. Diment, J. Löfgren, M. Alopaeus, M. Stosiek, M.-J. Cho, C. Xu, M. Hummel, D. Rigo, P. Rinke, and M. Balakshin, ChemSusChem 18, e202401711 (2025)
DOI: DOI.org/10.1002/cssc.202401711
31. Active learning of molecular data for task-specific objectives, K. Ghosh, M. Todorović, A. Vehtari, and P. Rinke, J. Chem. Phys. 162, 014103 (2025)
DOI: DOI.org/10.1063/5.0229834
30. Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning, F. Bortolussi, H. Sandström, F. Partovi, J. Mikkilä, P. Rinke, and M. Rissanen, Atmos. Chem. Phys. 25, 685 (2025)
DOI: DOI.org/10.5194/acp-25-685-2025
29. Precision benchmarks for solids: G0W0 calculations with different basis sets, M. Azizi, F. A. Delesma, M. Giantomassi, D. Zavickis, M. Kuisma, K. Thyghesen, D. Golze, A. Buccheri, M.-Y. Zhang, P. Rinke, C. Draxl, A. Gulans, and Xavier Gonze, Comput. Mater. Sci. 250, 113655 (2025)
DOI: DOI.org/10.1016/j.commatsci.2024.113655
28. Data-efficient optimization of thermally-activated polymer actuators through machine learning, Y. Zhang, M. Vaara, A. Alesafar, D. B. Nguyen, P. Silva, L. Koskelo, J. Ristolainen, M. Stosiek, J. Löfgren, J. Vapaavuori, and P. Rinke, Mater. Des. 253, 113908 (2025)
DOI: DOI.org/10.1016/j.matdes.2025.113908
27. Efficient dataset generation for machine learning halide perovskite alloys, H. Homm, J. Laakso, and P. Rinke, Phys. Rev. Mater. 9, 053802 (2025)
DOI: DOI.org/10.1103/PhysRevMaterials.9.053802
26. Exploring noncollinear magnetic energy landscapes with Bayesian optimization, J. Baumsteiger, L. Celiberti, P. Rinke, M. Todorović, and C. Franchini, Digit. Discov. (2025)
DOI: DOI.org/10.1039/D4DD00402G
25. SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood, M. Alopaeus, M. Stosiek, D. Diment, J. Löfgren, M.-J. Cho, J. Hemming, T. Tirri, A. Pranovich, P. C. Eklund, D. Rigo, M. Balakshin, C. Xu, and P. Rinke, Sci Data 12, 996 (2025)
DOI: DOI.org/10.1038/s41597-025-05327-8
24. Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science, D. A. Egger, M. Grumet, T. Bučko, T. J. Chem. Phys. 160, 120901 (2025)
DOI: DOI.org/10.1063/5.0287358
23. Highly Crystalline and Oriented Thin Films of Fully Conjugated 3D-Covalent Organic Frameworks, I. Muñoz-Alonso, D. Bessinger, S. Reuter, M. Righetto, L. Fuchs, M. Döblinger, D. D. Medina, F. Ortmann, L. M. Herz, T. Bein Angew. Chem. Int. Ed. 2025, 64, e202505799
DOI: DOI.org/10.1002/anie.202505799
22. Light-Induced Fine-Tuning of Optical Cavities for Organic Optoelectronic Devices, S. Xing, E. Bittrich, V. Prifti, S. Buchholtz, Y. Liu, L. C. Winkler, M. F. X. Dorfner, M. Malanin, M. Wang, G. Liu, D. Samigullina, A.-L. Hofmann, J. Wolansky, J. Vahland, T. Zhang, S. D. Seddon, D. Fischer, S. Reineke, F. Ortmann, X. Feng, H. Kleemann, J. Benduhn, K. Leo, Nature Communications 2025, 16, 8426
DOI: DOI.org/10.1038/s41467-025-64272-7
21. Evaluating First-Principles Electron-Phonon Couplings: Consistency Across Methods and Implementations, K. Merkel, M. F. X. Dorfner, M. Engel, G. Kresse, F. Ortmann
J. Phys. Mater. 2025, 8, 045014
DOI: DOI.org/10.1088/2515-7639/ae0ef1
20. An improved guess for the variational calculation of charge-transfer excitations in large systems, N. Bogo, Z. Zhang, M. Head-Gordon, C. J. Stein, Phys. Chem. Chem. Phys., 27, 17533-17547. (2025)
DOI: DOI.org/10.1039/D5CP01867F
19. Efficient Electronic-Structure Methods Toward Catalyst Screening: Projection-Based Embedding Theory for CO2 Reduction Reaction Intermediates, E. Kolodzeiski, C. J. Stein, Angew. Chem. Int. Ed., 64, e202503418. (2025)
DOI: DOI.org/10.1002/anie.202503418
18. Understanding Electronic Excitations Between Single Determinants with Occupied-Virtual Orbitals for Chemical Valence, H. Shen, N. Bogo, C. J. Stein, M. Head-Gordon, J. Chem. Theory Comput., 21, 19, 9525-9537. (2025)
DOI: DOI.org/10.1021/acs.jctc.5c01029
17. Optimizing Extended Tight-Binding Methods for Metal-Surface Interactions, S. Moradi, P. Dabbaghi, C. J. Stein. Chem. Phys. Chem., 26, e202500463. (2025)
DOI: DOI.org/10.1002/cphc.202500463
16. Catalytic Enantioselective [6π] Photocyclization Reactions by Chromophore Activation with a Chiral Lewis Acid, D. Grünwald, C. M. Jayakumari, N. Jeremias, J. Zuber, C. J. Stein, T. Bach, J. Am. Chem. Soc., 147, 50, 46525-46534. (2025)
DOI: DOI.org/10.1021/jacs.5c17390
15. Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution, Julia A. Stebani, Iñigo Iribarren Aguirre, Gohar A. Siddiqui, Darren Wragg, Alessio Gagliardi and Angela Casini (2025) J. Chem. Theory Comput. 2025, 21, 23, 12278–12288
DOI: 10.1021/acs.jctc.5c01465
14. Auristor: Self-Learning Edge States from Chirality-Breaking Growth of Graphene Nanoribbons–A Computational Study of Resonant Scattering and Memcapacitive Behavior, Aykut Turfanda, Alessio Gagliardi (2025).The Journal of Physical Chemistry C, 129(34), 15393-15408
DOI: DOI.org/10.1021/acs.jpcc.5c03062
13. A Kinetic Monte Carlo Model to Understand Limiting Regimes of Photocurrent and Interfacial Charge Transfer in Organic Semiconductor–Electrolyte Interfaces, S. Wang, M. Gößwein, G. Tullii, C. Marzuoli, M. R. Antognazza, A. Gagliardi, Advanced Materials Interfaces 2025, 12, e00343
DOI: DOI.org/10.1002/admi.202500343
12. Drift-Diffusion Modeling of Perovskite Solar Cells: Past and Future Possibilities, A. Singh, A. Gagliardi EES Solar 2025, 1, 694–711
DOI: DOI.org/10.1039/D5EL00040H
11. Comparative Convolutional Neural Networks for Perovskite Solar Cell PCE Predictions, M. Harth, D. Kishore Kumar, S. Kassou, K. El Idrissi, R. K. Gupta, Y. Daniel, O. Makdasi, I. Visoly-Fisher, A. Gagliardi npj Computational Materials 2025, 11, 251
DOI: DOI.org/10.1038/s41524-025-01744-w
10. Process Parameter Specification and Control in Solution Processing of Hybrid Perovskite Photovoltaics: From Domain-Specific Jargon to Evidence-Based, Unambiguous Description of Experimental Workflows, S. Ternes, C. J. Brabec, L. A. Castriotta, T. Exlager, K. Forberich, A. Gagliardi, M. Götte, F. Mathies, S. R. Ratnasingham, L. K. Reb, E. Unger, A. Di Carlo, Advanced Energy Materials 2025, 15, e03187
DOI: DOI.org/10.1002/aenm.202503187
9. Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data, Maximilian Stupp, P. S. Koutsourelakis, Journal of Chemical Theory and Computation, Vol 22/Issue 1, October 24, 2025
DOI: DOI.org/10.1021/acs.jctc.5c01504
8. Enhancing Machine Learning Potentials Through Transfer Learning Across Chemical Elements, S. Röcken, J. Zavadlav (2025), Journal of Chemical Information and Modeling
DOI: 10.1021/acs.jcim.5c00293
7. Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration, P. Fuchs, M. Sanocki, J. Zavadlav (2025), npj Computational Materials 11 (1)
DOI: DOI.org/10.1038/s41524-025-01790-4
6. chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations, P. Fuchs, W. Chen, S. Thaler, J. Zavadlav (2025), Journal of Chemical Theory and Computation 21 (15), 7550-7560
DOI: 10.1021/acs.jctc.5c00996
5. Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials, W. Chen, F. Görlich, P. Fuchs, J. Zavadlav (2025), Journal of Chemical Theory and Computation
DOI: 10.1021/acs.jctc.5c01712
2024
4. Conductive filament distribution in nano-scale electrochemical metallization cells, M. Speckbacher, M. Rinderle, O. Bienek, I. D. Sharp, A. Gagliardi, M. Tornow, Nanoscale 16, 19675–19682 (2024)
DOI: DOI.org/10.1039/d4nr02870h
3. Predicting solvation free energies with an implicit solvent machine learning potential, S. Röcken, A. F. Burnet, J. Zavadlav, J. Chem. Phys. 161(23), (2024)
DOI: dx.DOI.org/10.1063/5.0235189
2. Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors, W. O’Leary et al., J. Am. Chem. Soc. 146, 26863 (2024)
DOI: pubs.acs.org/DOI/full/10.1021/jacs.4c07812
1. Beyond Cation Disorder: Site Symmetry-Tuned Optoelectronic Properties of the Ternary Nitride Photoabsorber ZrTaN3, E. Sirotti et al., Adv. Energy Mater. 14, 2402540 (2024)
DOI: DOI.org/10.1002/aenm.202402540