气候变化领域集成服务门户(CCPortal): Designing self-assembling kinetics with differentiable statistical physics models

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DOI	10.1073/pnas.2024083118
	Designing self-assembling kinetics with differentiable statistical physics models
	Goodrich C.P.; King E.M.; Schoenholz S.S.; Cubuk E.D.; Brenner M.P.
发表日期	2021
ISSN	00278424
卷号	118 期号:10
英文摘要	The inverse problem of designing component interactions to target emergent structure is fundamental to numerous applications in biotechnology, materials science, and statistical physics. Equally important is the inverse problem of designing emergent kinetics, but this has received considerably less attention. Using recent advances in automatic differentiation, we show how kinetic pathways can be precisely designed by directly differentiating through statistical physics models, namely free energy calculations and molecular dynamics simulations. We consider two systems that are crucial to our understanding of structural self-assembly: bulk crystallization and small nanoclusters. In each case, we are able to assemble precise dynamical features. Using gradient information, we manipulate interactions among constituent particles to tune the rate at which these systems yield specific structures of interest. Moreover, we use this approach to learn nontrivial features about the high-dimensional design space, allowing us to accurately predict when multiple kinetic features can be simultaneously and independently controlled. These results provide a concrete and generalizable foundation for studying nonstructural self-assembly, including kinetic properties as well as other complex emergent properties, in a vast array of systems. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Colloids; Inverse design; Self-assembly
语种	英语
scopus关键词	article; calculation; colloid; crystallization; kinetics; molecular dynamics; physics
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180412
作者单位	School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States; Institute of Science and Technology Austria, Klosterneuburg, A-3400, Austria; Physics Department, Harvard University, Cambridge, MA 02138, United States; Brain Team, Google Research, Mountain View, CA 94043, United States
推荐引用方式 GB/T 7714	Goodrich C.P.,King E.M.,Schoenholz S.S.,et al. Designing self-assembling kinetics with differentiable statistical physics models[J],2021,118(10).
APA	Goodrich C.P.,King E.M.,Schoenholz S.S.,Cubuk E.D.,&Brenner M.P..(2021).Designing self-assembling kinetics with differentiable statistical physics models.Proceedings of the National Academy of Sciences of the United States of America,118(10).
MLA	Goodrich C.P.,et al."Designing self-assembling kinetics with differentiable statistical physics models".Proceedings of the National Academy of Sciences of the United States of America 118.10(2021).