5 papers
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Panagiotis Antoniadis, Beatrice Pavesi, Simon Olsson +1
Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over high-d…
HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passing
Johann Flemming Gloy, Simon Olsson
Flow and diffusion-based models have emerged as powerful tools for scientific applications, particularly for sampling non-normalized probability distributions, as exemplified by Bo…
Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
Juan Viguera Diez, Mathias Schreiner, Simon Olsson
Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations…
Generative flow-based warm start of the variational quantum eigensolver
Hang Zou, Martin Rahm, Anton Frisk Kockum +1
Hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) show promise for quantum simulations on near-term quantum devices, but are often limited by compl…
Boltzmann priors for Implicit Transfer Operators
Juan Viguera Diez, Mathias Schreiner, Ola Engkvist +1
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task…