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The Journal of Chemical Physics|July 10, 2023
Energy-based clustering: Fast and robust clustering of data with known likelihood functionsMoritz Thürlemann, Sereina RinikerChemical Science|November 29, 2023
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systemsMoritz Thürlemann, Sereina RinikerJournal of Chemical Theory and Computation|January 12, 2023
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular InteractionsMoritz Thürlemann, Lennard Böselt, Sereina RinikerJournal of Chemical Theory and Computation|April 5, 2021
Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase SystemsLennard Böselt, Moritz Thürlemann, Sereina RinikerJournal of Chemical Theory and Computation|February 3, 2022
Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural NetworksMoritz Thürlemann, Lennard Böselt, Sereina RinikerJournal of the American Chemical Society|February 17, 2025
Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in SolutionFelix Pultar, Moritz Thürlemann, Igor Gordiy, et al.Journal of the American Chemical Society|July 1, 2026
Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic ReactionsMoritz Thürlemann, Felix Pultar, Igor Gordiy, et al.Pageof 1