Self-Assembly: From Blueprints to Breakthroughs
Self-assembly, the process by which building blocks spontaneously organize into larger structures, is a pervasive phenomenon in soft matter systems, from the organization of biological matter to the development of advanced functional materials. Interdisciplinary efforts, combining advances in material synthesis, computational approaches, and, more recently, machine learning, are uncovering innovative strategies to control these processes.
Self-assembly, the process by which building blocks spontaneously organize into larger structures, is a pervasive phenomenon in soft matter systems, from the organization of biological matter to the development of advanced functional materials. Interdisciplinary efforts, combining advances in material synthesis, computational approaches, and, more recently, machine learning, are uncovering innovative strategies to control these processes.Yet, despite tremendous progress, self-assembly remains far from fully understood. Experimental and numerical studies reveal that competition between different structures—both ordered and amorphous—and kinetic bottlenecks, such as phase separation, gelation, and glass formation, often hinder the efficient formation of the desired structures.
This special issue of The Journal of Chemical Physics invites contributions from both theoretical and experimental perspectives, with the goal of bringing us closer to mastering the design of systems that can self-assemble with precision and predictability.
Guest Editors
Laura Filion, Utrecht University
Daniela Kraft, Leiden University
Beth Lindquist, Lawrence Berkeley National Laboratory
John Russo, Sapienza University of Rome
Robert Jack, University of Cambridge
Sal Torquato , Princeton University
Wataru Shinoda, Okayama University
Shuichi Hiraoka, University of Tokyo
Oleg Gang, Columbia University