Frank Smallenburg
Université Paris-Saclay
101 Papers
240 Citations
Frank Smallenburg is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Phase diagram & Monte Carlo method. The author has an hindex of 26, co-authored 84 publications. Previous affiliations of Frank Smallenburg include University of Düsseldorf & Sapienza University of Rome.
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Papers
Entropy-driven formation of large icosahedral colloidal clusters by spherical confinement
Bart de Nijs,Simone Dussi,Frank Smallenburg,Johannes D. Meeldijk,Dirk J. Groenendijk,Laura Filion,Arnout Imhof,Alfons van Blaaderen,Marjolein Dijkstra +8 more
TL;DR: It is reported that entropy and spherical confinement suffice for the formation of icosahedral clusters consisting of up to 100,000 particles that are entropically favoured over the bulk face-centred cubic crystal structure.
311
Efficient method for predicting crystal structures at finite temperature: variable box shape simulations.
Laura Filion,Matthieu Marechal,Bas van Oorschot,Daniël M. Pelt,Frank Smallenburg,Marjolein Dijkstra +5 more
TL;DR: An efficient and robust method based on Monte Carlo simulations for predicting crystal structures at finite temperature for hard, attractive, and anisotropic interactions, and predicts new crystal structures for hard asymmetric dumbbell particles, bowl-like particles and hard oblate cylinders.
Patchy particle model for vitrimers.
TL;DR: A patchy particle model whose dynamics directly mimic the bond exchange mechanism and reproduce the observed glass-forming ability is introduced, bringing new insight into the swelling behavior of vitrimers in solvents.
117
Tuning the Liquid-Liquid Transition by Modulating the Hydrogen-Bond Angular Flexibility in a Model for Water.
TL;DR: This study definitively proves that the liquid-liquid transition in the ST2 model is a genuine phenomenon, of high relevance in all tetrahedral network-forming liquids, including water.
Autonomously revealing hidden local structures in supercooled liquids
Emanuele Boattini,Susana Marín-Aguilar,Saheli Mitra,Giuseppe Foffi,Frank Smallenburg,Laura Filion +5 more
TL;DR: The power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials, are demonstrated.