Soft Matter Roadmap

IF 4.9 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Jean-Louis Barrat, Emanuela Del Gado, Stefan U. Egelhaaf, Xiaoming Mao, Marjolein Dijkstra, David J Pine, Sanat K Kumar, Kyle Bishop, Oleg Gang, Allie Obermeyer, Christine M Papadakis, Costantinos Tsitsilianis, Ivan I Smalyukh, Aurelie Hourlier-Fargette, Sebastien Andrieux, Wiebke Drenckhan, Norman Wagner, Ryan P. Murphy, Eric R. Weeks, Roberto Cerbino, Yilong Han, Luca Cipelletti, Laurence Ramos, Wilson C K Poon, James A. Richards, Itai Cohen, Eric M. Furst, Alshakim Nelson, Stephen L Craig, Rajesh Ganapathy, Ajay Kumar Sood, Francesco Sciortino, M Mungan, Srikanth Sastry, Colin Scheibner, Michel fruchart, Vincenzo Vitelli, S. A. Ridout, M. Stern, I. Tah, G. Zhang, Andrea J Liu, Chinedum O. Osuji, Yuan Xu, Heather M. Shewan, Jason Stokes, Matthias Merkel, Pierre Ronceray, Jean-François Rupprecht, Olga Matsarskaia, Frank Schreiber, Felix Roosen-Runge, Marie-Eve Aubin-Tam, Gijsje Koenderink, Rosa M. Espinosa-Marzal, Joaquin Yus, Jiheon Kwon
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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 characterize 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.

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引用次数: 0

Abstract

Abstract Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, 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 characterize 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 paper 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 characterization, instrumental, simulation and theoretical methods as well as general concepts.
软物质路线图
软材料通常被定义为由介观实体构成的材料,通常自组织,对热波动和弱扰动敏感。典型的例子是胶体、聚合物、两亲体、液晶、泡沫。软材料在日常商品和技术应用中的重要性是巨大的,控制或改善其性能是许多努力的重点。 从基本的角度来看,通过调整成分之间的相互作用和施加外部扰动来操纵软材料性质的可能性,会产生几乎无限的物理性质变化。再加上它们相对容易观察和表征,这使得软物质系统成为研究统计物理现象的强大模型系统,其中许多也与硬凝聚物质系统相关。理解中尺度成分的新特性仍然面临着巨大的挑战,这激发了大量新的实验方法,包括合成具有定制自组装特性的新系统,或在成像、散射或流变学方面的新实验技术。理论和数值方法以及粗粒度模型已经成为预测软质材料物理性质的核心,而使用机器学习工具的计算方法在许多研究中发挥着越来越重要的作用。本路线图文件旨在对软质材料领域最近和可能的未来活动进行广泛的概述。专家们涵盖了材料合成和表征、仪器、模拟和理论方法以及一般概念的各种发展和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JPhys Materials
JPhys Materials Physics and Astronomy-Condensed Matter Physics
CiteScore
10.30
自引率
2.10%
发文量
40
审稿时长
12 weeks
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