Dresden 2026 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 14: Machine Learning in Dynamics and Statistical Physics II

DY 14.4: Talk

Monday, March 9, 2026, 15:45–16:00, HÜL/S186

Learning order: can neural networks discover phase transitions without symmetry functions? — •Carina Karner — Institute for Theoretical Physics, TU Wien, Vienna, Austria

Phase transitions in soft matter systems from crystallization to gelation arise from collective particle rearrangements that are challenging to capture in full microscopic detail. Conventional approaches rely on order parameters or symmetry functions to characterize emerging structures, but such descriptors may overlook crucial features in the often complex organisation of biolgical materials or synthetic super-structures. Here we investigate whether machine learning can uncover these hidden features directly from raw particle configurations. Using autoencoders trained on simulated trajectories of serveral soft matter systems, we show that the latent space encodes clear signatures of structural transitions without the need for handcrafted inputs. Our results suggest that neural networks can serve as unbiased tools to detect and interpret phase behavior in complex soft matter systems, revealing patterns that elude traditional symmetry-based analysis.

Keywords: Equivariant neural networks; Symmetry-aware learning; Dimensionality reduction; Phase detection; Data compression