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Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
Winfried Ripken, Michael Plainer, Gregor Lied +5
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a f…
Generative Pseudo-Force Fields for Molecular Generation
Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4
Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative…
Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
Michael Plainer, Hao Wu, Leon Klein +2
In recent years, diffusion models trained on equilibrium molecular distributions have proven effective for sampling biomolecules. Beyond direct sampling, the score of such a model…
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
Yuanqi Du, Michael Plainer, Rob Brekelmans +5
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large sp…