Material Learning

Abstract

The strong increase in digital computing power in combination with the availability of large amounts of data has led to a revolution in machine learning. Computers now exhibit superhuman performance in activities such as pattern recognition and board games. However, the implementation of machine learning in digital computers is intrinsically wasteful, with energy consumption becoming prohibitively high for many applications. For that reason, people have started looking at natural information processing systems, in particular the brain, that operate much more efficiently. Whereas the brain utilizes wet, soft tissue for information processing, one could in principle exploit any material and its physical properties to solve a problem. Here we give examples of how nanomaterial networks can be trained using the principle of material learning to take full advantage of the computational power of matter¹.

We have shown that a designless network of gold nanoparticles can be configured into Boolean logic gates using artificial evolution². We further demonstrated that this principle is generic and can be transferred to other material systems. By exploiting the nonlinearity of a nanoscale network of boron dopants in silicon, we can significantly facilitate classification. Using a convolutional neural network approach, it becomes possible to use our device for handwritten digit recognition³. An alternative material learning is approach is followed by first mapping our Si:B network on a deep neural network model, which allows for applying standard machine learning techniques in finding functionality⁴. Finally, we show that the widely applied machine learning technique of gradient descent can be directly applied in materio, opening up the pathway for autonomously learning hardware systems⁵.

References

[1] C. Kaspar et al. Nature 594, 345, 2021

[2] S.K. Bose et al. Nature Nanotechnol. 10, 1048, 2015

[3] T. Chen et al. Nature 577, 341, 2020

[4] H.-C. Ruiz Euler et al. Nature Nanotechnol. 15, 992 2020

[5] M.N. Boon et al. arxiv.org/abs/2105.11233, 2021