Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning (Adv. Mater. 3/2022)

Shin, Dongil; Cupertino, Andrea; Jong, Matthijs H. J. de; Steeneken, Peter G.; Bessa, Miguel A.; Norte, Richard A.

doi:10.1002/adma.202270019

Cited by 4 publications

(10 citation statements)

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“…σ ex is an extra contribution possibly given by initial strain, as in the case of strongly pre‐stressed materials such as Si 3 N 4 . [ 11,47,48 ] The constituent materials of the membrane, namely Si 3 N 4 and Au, are fully isotropic and can be described by the Young's modulus E and Poisson's ratio ν, which are embedded inside the compliance tensor S . The material density ρ completes the set of parameters needed for the simulation.…”

Section: Discussionmentioning

confidence: 99%

“…Along with micro‐ and nano‐structuration, the miniaturization of whole mechanical resonators has been pushed to the micrometric scale; among the others, a wide investigation has interested nanometer‐thick, micrometer‐wide membranes. [ 6–8 ] Thanks to their large quality factors [ 6,9–11 ] and extreme aspect ratio, these kinds of device have been used as a standard platform for classical and quantum effects in optomechanics [ 9,12 ] and for light, [ 13 ] pressure, [ 14 ] mass, [ 15 ] and other sensing applications. [ 7,16 ] The mechanical membranes are an ideal platform for hosting photonic metasurfaces, which can be introduced by periodically patterning a portion of the device.…”

Section: Introductionmentioning

confidence: 99%

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Mechanical Mode Engineering with Orthotropic Metamaterial Membranes

Conte

Vicarelli

Zanotto

et al. 2022

Adv Materials Technologies

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Metamaterials are structures engineered at a small scale with respect to the wavelength of the excitations they interact with. These structures behave as artificial materials whose properties can be chosen by design, mocking and even outperforming natural materials, and making them the quintessential tool for manipulation of wave systems. In this paper it is shown how the acoustic properties of a silicon nitride membrane can be affected by nanopatterning. The degree of asymmetry in the pattern geometry induces an artificial anisotropic elasticity, resulting in the splitting of otherwise degenerate mechanical modes. The artificial material introduced has a maximum Ledbetter–Migliori anisotropy of 1.568, favorably comparing to most bulk natural crystals. With an additional freedom in defining arbitrary asymmetry axes by pattern rotation, the described approach can be useful for fundamental investigation of material properties as well as for devising improved sensors of light, mass, or acceleration based on micromechanical resonators.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanical Mode Engineering with Orthotropic Metamaterial Membranes

Conte

Vicarelli

Zanotto

et al. 2022

Adv Materials Technologies

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“…However, using analytical or computational models to generate data is insufficient when the metamaterial involves high nonlinearity, where such models could provide only limited accuracies. [37] While some previous studies [58,59] suggested experimental data as an alternative source, data-driven methods to design elastomer metamaterials relying purely on experimental measurements have not been demonstrated to date. Our work demonstrates the possibility of designing highly nonlinear metamaterials using predominantly experimental data with minimal knowledge of the underlying physics.…”

Section: Discussionmentioning

confidence: 99%

“…For example, for applications where property predictions are computationally costly, Bayesian optimization could reduce the total number of measurements by dynamically controlling the measurement schemes. [58][59][60] By combining the GP model with the CMA-ES design optimizer, we can successfully design these nonlinear elastomer materials without requiring complete physical models. Traditional physics-driven metamaterial design is unsuitable for chi springs, as they rely on asymmetrical buckling of extensible trusses along with self-contact, which is highly nonlinear and challenging to build a complete model.…”

Section: Discussionmentioning

confidence: 99%

Modeling and Design of Zero‐Stiffness Elastomer Springs Using Machine Learning

Kim

Tawfick

King

2022

Advanced Intelligent Systems

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Architected metamaterials exhibit complex stress–strain responses, such as quasi‐zero stiffness (QZS) plateau response useful in vibration and energy absorption, by exploiting elastic instabilities such as buckling. However, the design of metamaterials with prescribed nonlinear mechanical properties is challenging due to the difficulty in accurately predicting nonlinear mechanical responses such as buckling and self‐contact, as well as geometric and material uncertainties. Herein, additive manufacturing (AM) with machine learning (ML) to demonstrate data‐driven modeling and the design of nonlinear elastomeric springs with a broad stress plateau is combined. 243 elastomer chi (χ) spring parts with different design parameters fabricated by AM are tested. A Gaussian process (GP) model is trained on key features of the measured mechanical response and predicts the mechanical response within a 3% error with as few as 97 parts. To account for the geometric and material uncertainties, the GP model to develop an uncertainty‐aware design optimization approach to tailor the mechanical response based on the covariance matrix adaptation evolution strategy (CMA‐ES) is utilized. Excellent agreement is demonstrated between the designed response and measured response over the range of plateau stress considered. The research illustrates the promise of experimental data‐driven approaches and AM in enabling complex material design.

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“…With the emergence of nano‐ and micro‐electromechanical systems, and the drive toward quantum limited mechanical elements, pushing the performance boundaries of resonators has become a matter of high scientific and societal relevance. [ 1–6 ] In particular, mechanical energy loss via the clamping points has become a dominant factor, limiting the Q of these resonators. As a consequence, attention has moved toward the field of levitodynamics.…”

Section: Introductionmentioning

confidence: 99%

Diamagnetic Composites for High‐Q Levitating Resonators

et al. 2022

Self Cite

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Levitation offers extreme isolation of mechanical systems from their environment, while enabling unconstrained high-precision translation and rotation of objects. Diamagnetic levitation is one of the most attractive levitation schemes because it allows stable levitation at room temperature without the need for a continuous power supply. However, dissipation by eddy currents in conventional diamagnetic materials significantly limits the application potential of diamagnetically levitating systems. Here, a route toward high-Q macroscopic levitating resonators by substantially reducing eddy current damping using graphite particle based diamagnetic composites is presented. Resonators that feature quality factors Q above 450 000 and vibration lifetimes beyond one hour are demonstrated, while levitating above permanent magnets in high vacuum at room temperature. The composite resonators have a Q that is >400 times higher than that of diamagnetic graphite plates. By tuning the composite particle size and density, the dissipation reduction mechanism is investigated, and the Q of the levitating resonators is enhanced. Since their estimated acceleration noise is as low as some of the best superconducting levitating accelerometers at cryogenic temperatures, the high Q and large mass of the presented composite resonators positions them as one of the most promising technologies for next generation ultra-sensitive room temperature accelerometers.

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Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning (Adv. Mater. 3/2022)

Cited by 4 publications

References 0 publications

Mechanical Mode Engineering with Orthotropic Metamaterial Membranes

Mechanical Mode Engineering with Orthotropic Metamaterial Membranes

Modeling and Design of Zero‐Stiffness Elastomer Springs Using Machine Learning

Diamagnetic Composites for High‐Q Levitating Resonators

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