# Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning

arXiv: https://arxiv.org/abs/2108.04809 · HTML: https://mlsanity.com/paper/2108.04809/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 82 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Bayesian optimization for materials design](https://mlsanity.com/paper/1506.01349/) (2015)

## Built on by (most cited first, 6 of 6)

- [Learning Mechanically Driven Emergent Behavior with Message Passing Neural Networks](https://mlsanity.com/paper/2202.01380/) (2022)
- [Investigating Deep Learning Model Calibration for Classification Problems in Mechanics](https://mlsanity.com/paper/2212.00881/) (2022)
- [Pentagonal Photonic Crystal Mirrors: Scalable Lightsails with Enhanced Acceleration via Neural Topology Optimization](https://mlsanity.com/paper/2407.07896/) (2024)
- [Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling](https://mlsanity.com/paper/2507.13416/) (2025)
- [Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures](https://mlsanity.com/paper/2507.22079/) (2025)
- [Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning](https://mlsanity.com/paper/2408.08428/) (2024)

## Abstract (arXiv)

From ultra-sensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room temperature environments. Currently, silicon nitride nanoresonators stand as a leading microchip platform in these advances by allowing for mechanical resonators whose motion is remarkably isolated from ambient thermal noise. However, to date, human intuition has remained the driving force behind design processes. Here, inspired by nature and guided by machine learning, a spiderweb nanomechanical resonator is developed that exhibits vibration modes which are isolated from ambient thermal environments via a novel "torsional soft-clamping" mechanism discovered by the data-driven optimization algorithm. This bio-inspired resonator is then fabricated; experimentally confirming a new paradigm in mechanics with quality factors above 1 billion in room temperature environments. In contrast to other state-of-the-art resonators, this milestone is achieved with a compact design which does not require sub-micron lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. Here we demonstrate the ability of machine learning to work in tandem with human intuition to augment creative possibilities and uncover new strategies in computing and nanotechnology.