activity
20032023
most citedPushing the glass transition towards random close packing using self-propelled hard spheres

199 citations · 3.7k across the 90 of their papers we have counts for

collaborators

91 papers

cond-mat.soft2023★ 16 cited

Brute-force nucleation rates of hard spheres compared with rare-event methods and classical nucleation theory

Willem Gispen, Marjolein Dijkstra

We determine the nucleation rates of hard spheres using brute-force molecular dynamics simulations. We overcome nucleation barriers of up to , leading to a rigorous test…

cond-mat.soft2023

A colloidal viewpoint on the finite sphere packing problem: the sausage catastrophe

Susana Marín-Aguilar, Fabrizio Camerin, Stijn van der Ham +3

It is commonly believed that the most efficient way to pack a finite number of equal-sized spheres is by arranging them tightly in a cluster. However, mathematicians have conjectur…

cond-mat.soft2023★ 95 cited

Colloidal Hard Spheres: Triumphs, Challenges and Mysteries

C. Patrick Royall, Patrick Charbonneau, Marjolein Dijkstra +4

The simplicity of hard spheres as a model system is deceptive. Although the particles interact solely through volume exclusion, that nevertheless suffices for a wealth of static an…

cond-mat.stat-mech2023★ 7 cited

A Boltzmann generator for the isobaric-isothermal ensemble

Steyn van Leeuwen, Alberto Pérez de Alba Ortíz, Marjolein Dijkstra

Boltzmann generators (BGs) are now recognized as forefront generative models for sampling equilibrium states of many-body systems in the canonical ensemble, as well as for calculat…

cond-mat.soft2023★ 2 cited

Flexible Colloidal Molecules with Directional Bonds and Controlled Flexibility

Yogesh Shelke, Fabrizio Camerin, Susana Marín-Aguilar +3

Colloidal molecules are ideal model systems for mimicking real molecules and can serve as versatile building blocks for the bottom-up self-assembly of flexible and smart materials.…

cond-mat.dis-nn2022

Dynamics of supercooled liquids from static averaged quantities using machine learning

Simone Ciarella, Massimiliano Chiappini, Emanuele Boattini +2

We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. Compared to techniques based on parti…