# Not so simple machines: Cracking the code for materials that can learn

**URL:** https://community.materialssquare.com/t/not-so-simple-machines-cracking-the-code-for-materials-that-can-learn/1017
**Category:** News
**Created:** [December 9, 2024, 3:58pm UTC](https://community.materialssquare.com/t/not-so-simple-machines-cracking-the-code-for-materials-that-can-learn/1017 "2024-12-09T15:58:17Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)
#### Post date: [December 9, 2024, 3:58pm UTC](https://community.materialssquare.com/t/not-so-simple-machines-cracking-the-code-for-materials-that-can-learn/1017/1 "2024-12-09T15:58:17Z")

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This article explores groundbreaking research by the University of Michigan on mechanical neural networks (MNNs), materials capable of learning and performing computations. By adapting the backpropagation algorithm used in digital neural networks, researchers trained physical lattices to respond to inputs like force and modify their properties to achieve desired outputs. These 3D-printed lattices, modeled after neural connections, adjust segment stiffness to solve tasks such as species identification or complex mechanical responses. This innovation hints at a future where materials, like airplane wings, could autonomously optimize themselves for changing conditions. The approach also offers insights into biological learning processes and paves the way for more advanced applications using materials like polymers and nanoparticle assemblies.

For more details, please continue reading the full article under the following link:

> **[Not so simple machines: Cracking the code for materials that can learn](https://phys.org/news/2024-12-simple-machines-code-materials.html)**
>
> It's easy to think that machine learning is a completely digital phenomenon, made possible by computers and algorithms that can mimic brain-like behaviors. But the first machines were analog and now, a small but growing body of research is showing...

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In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( [www.matsq.com](http://www.matsq.com) ), designed for streamlining the execution of materials and molecular atomistic simulations!

Best regards,

Dr. Gabriele Mogni  
Technical Consultant and EU Representative  
Virtual Lab Inc., the parent company of the Materials Square platform  
Website: [Home | Virtual Lab Inc.](http://www.virtuallab.co.kr/en/)  
Email: gabriele@simulation.re.kr

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