Publications SIMPLAIX

2025

• Remme R, Kaczun T, Ebert T, Gehrig CA, Geng D, Gerhartz G, Ickler MK, Klockow MV, Lippmann P, Schmidt JS, Wagner S, Dreuw A, Hamprecht FA (2025). Stable and Accurate Orbital-Free Density Functional Theory Powered by Machine Learning, J. Am. Chem. Soc. 147(32):28851-28859. 2231
• Teuffel J, Mukherjee G, Han SB, Elstner M, Wade RC (2025). On the determinants of electron transfer reorganization energy in a cytochrome P450: cytochrome b5 complex. A combined quantum mechanics and molecular dynamics simulation study, The Journal of Chemical Physics 162(19):195101. 2064
• Hoffmann DS, Dohmen PM, Sokolov M, Kleinekathöfer U, Elstner M (2025). Exciton Transfer Simulations in a Light-Harvesting 2 Complex Reveal the Transient Delocalization Mechanism, J. Phys. Chem. B 129(13):3345-3365. 2082
• Schopmans H, Friederich P (2025). Temperature-Annealed Boltzmann Generators, arXiv,cs.LG,2501.19077 2060
• Walter LJ, Bereau T (2025). Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining, Chem. Sci.,10.1039.D5SC03855C 2083

2024

• Han SB, Teuffel J, Mukherjee G, Wade RC (2024). Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome 2B4, Protein Science 33(10),e5165 1860
• Gryn’ova G, Bereau T, Müller C, Friederich P, Wade RC, Nunes-Alves A, Soares TA, Merz K (2024). EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning, J. Chem. Inf. Model. 2024, 64, 15, 5737–5738 1856
• Sokolov M, Hoffmann DS, Dohmen PM, Krämer M, Höfener S, Kleinekathöfer U, Elstner M (2024). Non-adiabatic molecular dynamics simulations provide new insights into the exciton transfer in the Fenna–Matthews–Olson complex, Phys. Chem. Chem. Phys., 2024,26, 19469-19496 1857
• Ghalami F, Dohmen PM, Krämer M, Elstner M, Xie W (2024). Nonadiabatic Simulation of Exciton Dynamics in Organic Semiconductors Using Neural Network-Based Frenkel Hamiltonian and Gradients, J. Chem. Theory Comput. 2024, 20, 14, 6160–6174 1858
• Riedmiller K, Reiser P, Bobkova E, Maltsev K, Gryn’ova G, Friederich P, Gräter F (2024). Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins, Chem. Sci.,10.1039.D3SC03922F 1781
• Zhou C, Neubert M, Koide Y, Zhang Y, Vuong V, Schlöder T, Dehnen S, Friederich P (2024). PAL – Parallel active learning for machine-learned potentials, arXiv,cs.LG,2412.00401 2061

2023

• Remme R, Kaczun T, Scheurer M, Dreuw A, Hamprecht FA (2023). KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory, The Journal of Chemical Physics 159(14),144113 1822
• Fedorov R, Gryn’ova G (2023). Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks, J. Chem. Theory Comput. 19(15):4796-4814 1685

2022

• Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, Hoesel Cv, Schopmans H, Sommer T, Friederich P (2022). Graph neural networks for materials science and chemistry, Commun Mater 3(1),93 1686
• Lippmann P, Sanmartín EF, Hamprecht FA (2022). Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources, arXiv,cs.LG,2210.07702 1689