activity
20202026
most citedPredicting plasticity in disordered solids from structural indicators

220 citations · 361 across the 10 of their papers we have counts for

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Showing cond-mat.softShow all

7 papers · 1 filter

cond-mat.soft2026

Thermal softening of the yield stress in glasses with different stability

Tianze Wan, Ludovic Berthier

The yielding of amorphous solids has been extensively studied in the athermal limit, but the influence of thermal fluctuations is much less understood, in particular for glasses of…

cond-mat.soft2025★ 1 cited

Numerical investigation of the equilibrium Kauzmann transition in a two-dimensional atomistic glass

Gerhard Jung, Misaki Ozawa, Giulio Biroli +1

Dense liquids gradually transform into non-equilibrium amorphous solids as they pass through the experimental glass transition. Experimentally, ergodicity is lost because measureme…

cond-mat.soft2024★ 11 cited

Normalizing flows as an enhanced sampling method for atomistic supercooled liquids

Gerhard Jung, Giulio Biroli, Ludovic Berthier

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine…

cond-mat.soft2023★ 45 cited

Ductile-to-brittle transition and yielding in soft amorphous materials: perspectives and open questions

Thibaut Divoux, Elisabeth Agoritsas, Stefano Aime +37

Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical e…

cond-mat.soft2023★ 38 cited

Roadmap on machine learning glassy dynamics

Gerhard Jung, Rinske M. Alkemade, Victor Bapst +10

Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge when studying the glass transition. The absence…

cond-mat.soft2023★ 19 cited

Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning

Gerhard Jung, Giulio Biroli, Ludovic Berthier

We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic…

cond-mat.soft2020★ 220 cited

Predicting plasticity in disordered solids from structural indicators

D. Richard, M. Ozawa, S. Patinet +16

Amorphous solids lack long-range order. Therefore identifying structural defects -- akin to dislocations in crystalline solids -- that carry plastic flow in these systems remains a…