Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science, D. A. Egger, M. Grumet, T. Bučko, T. (2025, under review) DOI: https://arxiv.org/abs/2506.19595
Analysis of real-space transport channels for electrons and holes in halide perovskites, F. Vonhoff, M. J. Schilcher, D. R. Reichman, D. A. Egger (2025, under review) DOI: https://arxiv.org/abs/2505.19999
Thermodynamic potentials from a probabilistic view on the system-environment interaction energy, M. Rahbar, C. J. Stein (2025, under review) DOI: https://doi.org/10.48550/arXiv.2505.00188
An improved guess for the variational calculation of charge-transfer excitations in large systems, N. Bogo, Z. Zhang, M. Head-Gordon, C. J. Stein (2025, under review) DOI: https://doi.org/10.48550/arXiv.2505.12645
2025
Similarity-based analysis of atmospheric organic compounds for machine learning applications, H. Sandström and P. Rinke, Geosci. Model Dev. 18, 2701 (2025) DOI: https://doi.org/10.5194/gmd-18-2701-2025
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: https://doi.org/10.1038/s41597-025-05327-8
Exploring noncollinear magnetic energy landscapes with Bayesian optimization, J. Baumsteiger, L. Celiberti, P. Rinke, M. Todorović, and C. Franchini, Digit. Discov. (2025) DOI: https://doi.org/10.1039/D4DD00402G
Efficient dataset generation for machine learning halide perovskite alloys, H. Homm, J. Laakso, and P. Rinke, Phys. Rev. Mater. 9, 053802 (2025) DOI: https://doi.org/10.1103/PhysRevMaterials.9.053802
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: https://doi.org/10.1016/j.matdes.2025.113908
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: https://doi.org/10.1016/j.commatsci.2024.113655
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: https://doi.org/10.5194/acp-25-685-2025
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: https://doi.org/10.1063/5.0229834
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, ChemSusChem18, e202401711 (2025) DOI: https://doi.org/10.1002/cssc.202401711
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: https://pubs.acs.org/doi/10.1021/acsenergylett.5c01519
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: https://dx.doi.org/10.1016/j.cpc.2025.109512
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: https://doi.org/10.1038/s41524-025-01630-5
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: https://doi.org/10.1021/acsaelm.5c00061
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: https://doi.org/10.1039/d5ee01052g
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: https://doi.org/10.1002/anie.202500524
2024
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: https://doi.org/10.1002/aenm.202402540
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: https://pubs.acs.org/doi/full/10.1021/jacs.4c07812
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: https://dx.doi.org/10.1063/5.0235189
Conductive filament distribution in nano-scale electrochemical metallization cells, M. Speckbacher, M. Rinderle, O. Bienek, I. D. Sharp, A. Gagliardi, M. Tornow, Nanoscale16, 19675–19682 (2024) DOI: https://doi.org/10.1039/d4nr02870h
