Publikationen SIMPLAIX

2025

• Schopmans H, Friederich P (2025). Temperature-Annealed Boltzmann Generators, arXiv,cs.LG,2501.19077 2060

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