자료유형  

 학위논문

Control Number  

0016932165

International Standard Book Number  

9798379604844

Dewey Decimal Classification Number  

530

Main Entry-Personal Name  

King, Ella M.

Publication, Distribution, etc. (Imprint  

[S.l.] : Harvard University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(126 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Brenner, Michael P.

Dissertation Note  

Thesis (Ph.D.)--Harvard University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Despite tremendous advances in synthetic materials design, the complexity achievable in artificial systems is dwarfed by the complexity of living matter. One cause of this discrepancy is that biological systems fundamentally rely on precise control over not just structure, but also function, in micron-scale components. Examples range from kinetic proofreading in DNA to regulation of clathrin formation and on-command microtubule disassembly. Achieving comparable dynamic and non-equilibrium functional control in synthetic materials remains an outstanding challenge. Because biological systems that control these non-equilibrium functionalities exist, it must be possible to design synthetic materials with similarly rich and complex functions. However, the design space of out-of-equilibrium functionalities is vast and hard to explore. How do we design complex functional materials without the luxury of billions of years of evolution? Here, we leverage automatic differentiation, the tool underlying much of the dramatic success in machine learning and non-convex optimization, to develop methods for computational materials design, and demonstrate quantitative control over non-equilibrium functionality in self-assembled materials. We couple this computationally-driven approach with a parallel effort to extract more information from experimental data, towards the goal of making our designs experimentally realizable. We develop a novel algorithm for particle tracking in systems with highly correlated motion and introduce a method for inferring interaction potentials from stochastic trajectory data.

Subject Added Entry-Topical Term  

Condensed matter physics.

Index Term-Uncontrolled  

Synthetic materials

Index Term-Uncontrolled  

Kinetic proofreading

Index Term-Uncontrolled  

Stochastic trajectory data

Index Term-Uncontrolled  

Clathrin formation

Index Term-Uncontrolled  

Biological systems

Added Entry-Corporate Name  

Harvard University Physics

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642708