# Conformational control of mechanical networks

@article{Kim2019ConformationalCO, title={Conformational control of mechanical networks}, author={Jason Z. Kim and Zhixin Lu and Steven H. Strogatz and Danielle S. Bassett}, journal={Nature Physics}, year={2019}, pages={1-7} }

Understanding conformational change is crucial for programming and controlling the function of many mechanobiological and mechanical systems such as robots, enzymes and tunable metamaterials. These systems are often modelled as constituent nodes (for example, joints or amino acids) whose motion is restricted by edges (for example, limbs or bonds) to yield functionally useful coordinated motions (for example, walking or allosteric regulation). However, the design of desired functions is made…

## 16 Citations

Design of Large Sequential Conformational Change in Mechanical Networks

- Computer Science
- 2019

This work model mechanical systems as linkages of rigid bonds connected by jointsconnected by joints, and formulate a simple but powerful framework for designing full nonlinear coordinated motions using concepts from dynamical systems theory.

Modular representation and control of floppy networks

- Computer ScienceArXiv
- 2022

This work combines geometric rigidity and algebraic sparsity to provide a framework for identifying the zero-energy floppy modes via a representation that illuminates the underlying hierarchy and modularity of the network, and thence the control of its nestedness and locality.

Topological Elasticity of Flexible Structures

- Materials SciencePhysical Review X
- 2020

Flexible mechanical metamaterials possess repeating structural motifs that imbue them with novel, exciting properties including programmability, anomalous elastic moduli and nonlinear and robust…

Supervised learning through physical changes in a mechanical system

- EducationProceedings of the National Academy of Sciences
- 2020

A framework for supervised learning in thin, creased sheets that learn the desired force–response behavior by physically experiencing training examples and then respond correctly (generalize) to previously unseen test forces is proposed.

Network architecture of energy landscapes in mesoscopic quantum systems

- PhysicsNew Journal of Physics
- 2019

This work constructs networks where nodes represent accessible energy states and edges represent allowed transitions, and finds that the networks exhibit Rentian scaling, which is characteristic of efficient transportation systems in computer circuitry, neural circuitry, and human mobility, and can be used to measure the interconnection complexity of a network.

Training nonlinear elastic functions: nonmonotonic, sequence dependent and bifurcating.

- Computer ScienceSoft matter
- 2021

This paper demonstrates that a recent approach based on training allows responses that are inherently non-linear in the linear regime, and shows examples of elaborate nonlinear training paths that lead to the following functions.

Continual Learning of Multiple Memories in Mechanical Networks

- Materials Science
- 2020

This model shows how specific material properties allow continuous learning of new functions through deployment of the material itself, thus playing the role of Bayesian priors used in sparse statistical regression.

Rigidity-Controlled Crossover: From Spinodal to Critical Failure.

- PhysicsPhysical review letters
- 2020

This study indicates that in addition to the strength of quenched disorder, an appropriately chosen global measure of rigidity (connectivity) can be also used to tune the system to criticality.

Geometry for mechanics

- PhysicsNature Physics
- 2019

The mechanics of many materials can be modelled by a network of balls connected by springs. A bottom-up approach based on differential geometry now captures changes in mechanics upon network growth…

Curiosity as filling, compressing, and reconfiguring knowledge networks

- Computer Science
- 2022

This work quantitatively investigates information gap theory, compression progress theory, and the conformational change theory of curiosity, and develops a novel network theoretical perspective on intrinsically motivated information acquisition that may harmonize with or compel an expansion of the classical taxonomy of curiosity.

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