arXiv Machine Learning June 16, 2026 By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt

Learning Topological Representations for Molecular Dynamics

Read the original on arXiv Machine Learning →

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs

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