Role of the Range in the Fluid−Crystal Coexistence for a Patchy Particle Model

2009 ◽  
Vol 113 (46) ◽  
pp. 15133-15136 ◽  
Author(s):  
Flavio Romano ◽  
Eduardo Sanz ◽  
Francesco Sciortino
Soft Matter ◽  
2008 ◽  
Vol 4 (6) ◽  
pp. 1173 ◽  
Author(s):  
Silvia Corezzi ◽  
Cristiano De Michele ◽  
Emanuela Zaccarelli ◽  
Daniele Fioretto ◽  
Francesco Sciortino

Soft Matter ◽  
2016 ◽  
Vol 12 (3) ◽  
pp. 741-749 ◽  
Author(s):  
Zhan-Wei Li ◽  
You-Liang Zhu ◽  
Zhong-Yuan Lu ◽  
Zhao-Yan Sun

A simple and general mesoscale soft patchy particle model is proposed to investigate the aggregation behavior and mechanism of various types of soft patchy particles with tunable number, size, direction, and geometrical arrangement of the patches.


2007 ◽  
Vol 19 (32) ◽  
pp. 322101 ◽  
Author(s):  
Flavio Romano ◽  
Piero Tartaglia ◽  
Francesco Sciortino

2015 ◽  
Vol 108 (2) ◽  
pp. 219a
Author(s):  
Heinrich C.R. Klein ◽  
Johanna E. Baschek ◽  
Marvin A. Boettcher ◽  
Ulrich S. Schwarz

2021 ◽  
Vol 155 (3) ◽  
pp. 034902
Author(s):  
H. J. Jonas ◽  
S. G. Stuij ◽  
P. Schall ◽  
P. G. Bolhuis

Author(s):  
Sebastien Kerisit ◽  
Thiruvillamalai Mahadevan ◽  
Jincheng Du

2021 ◽  
Vol 8 (9) ◽  
Author(s):  
Duxin Chen ◽  
Yongzheng Sun ◽  
Guanbo Shao ◽  
Wenwu Yu ◽  
Hai-Tao Zhang ◽  
...  

The mechanisms inducing unpredictably directional switches in collective and moving biological entities are largely unclear. Deeply understanding such mechanisms is beneficial to delicate design of biologically inspired devices with particular functions. Here, articulating a framework that integrates data-driven, analytical and numerical methods, we investigate the underlying mechanism governing the coordinated rotational flight of pigeon flocks with unpredictably directional switches. Particularly using the sparse Bayesian learning method, we extract the inter-agent interactional dynamics from the high-resolution GPS data of three pigeon flocks, which reveals that the decision-making process in rotational switching flight performs in a more nonlinear manner than in smooth coordinated flight. To elaborate the principle of this nonlinearity of interactions, we establish a data-driven particle model with two potential wells and estimate the mean switching time of rotational direction. Our model with its analytical and numerical results renders the directional switches of moving biological groups more interpretable and predictable. Actually, an appropriate combination of natures, including high density, stronger nonlinearity in interactions, and moderate strength of noise, can enhance such highly ordered, less frequent switches.


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