Soft Matter Roadmap

Published

December 12, 2023

Author(s)

Jean-Louis Barrat, Emanuela Del Gado, Stefan Egelhaaf, Xiaoming Mao, Marjolein Dijkstra, David Pine, Sanat Kumar, Kyle Bishop, Oleg Gang, Allie Obermeyer, Christine Papadakis, Constantinos Tsitsilianis, Ivan Smalyukh, Aurelie Hourlier-Fargette, Sebastien Andrieux, Wiebke Drenckhan, Norman J. Wagner, Ryan Murphy, Eric Weeks, Yilong Han, Luca Cipelletti, Laurence Ramos, Wilson Poon, James Richards, Itai Cohen, Eric Furst, Alshakim Nelson, Stephen Craig, Rajesh Ganapathy, Ajay Sood, Francesco Sciortino, Muhittin Mungan, Srikanth Sastry, Colin Scheibner, Michel Fruchart, Vincenzo Vitelli, S. Ridout, M. Stern, I. Tah, G. Zhang, Andrea Liu, Chinedum Osuji, Yuan Xu, Heather Shewan, Jason Stokes, Matthias Merkel, Pierre Ronceray, Jean-Francois Rupprecht, Olga Matsarskaia, Frank Schreiber, Felix Rossen-Runge, Marie-Eve Aubin-Tam, G. Koenderink, Rosa Espinosa-Marzal, Joaquin Yus, Jiheon Kwon

Abstract

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

Citation

Journal of Physics: Materials

Volume

7

Issue

1

Pub Type

Journals

Download Paper

DOI Link

Citation

Barrat, J. , Del Gado, E. , Egelhaaf, S. , Mao, X. , Dijkstra, M. , Pine, D. , Kumar, S. , Bishop, K. , Gang, O. , Obermeyer, A. , Papadakis, C. , Tsitsilianis, C. , Smalyukh, I. , Hourlier-Fargette, A. , Andrieux, S. , Drenckhan, W. , Wagner, N. , Murphy, R. , Weeks, E. , Han, Y. , Cipelletti, L. , Ramos, L. , Poon, W. , Richards, J. , Cohen, I. , Furst, E. , Nelson, A. , Craig, S. , Ganapathy, R. , Sood, A. , Sciortino, F. , Mungan, M. , Sastry, S. , Scheibner, C. , Fruchart, M. , Vitelli, V. , Ridout, S. , Stern, M. , Tah, I. , Zhang, G. , Liu, A. , Osuji, C. , Xu, Y. , Shewan, H. , Stokes, J. , Merkel, M. , Ronceray, P. , Rupprecht, J. , Matsarskaia, O. , Schreiber, F. , Rossen-Runge, F. , Aubin-Tam, M. , Koenderink, G. , Espinosa-Marzal, R. , Yus, J. and Kwon, J. (2023), Soft Matter Roadmap, Journal of Physics: Materials, [online], https://dx.doi.org/10.1088/2515-7639/ad06cc (Accessed November 21, 2024)