How Big Data Helps Learn New Properties of Conventional Materials

Sometimes even substances and materials about which,It would seem that absolutely everything is known, they can surprise quite a lot. Moreover, in order to learn new properties of substances, it is not at all necessary to “study them from the inside”, taking apart each elementary particle that makes up these substances individually. For example, recently a group of researchers using machine learning technologies and big data was able to discover new properties of nickel.

Nickel is a fairly common material. But, as it turned out, we don’t know much about him.

What new properties does nickel have

According to a study published by the magazinePhysical Review, a group of researchers led by Edwin Fochtung, a professor of materials science and engineering at the Rensselaer Polytechnic Institute, has found a new way to work with nickel by "unlocking" its properties. Moreover, such a discovery allows you to use it in a huge pile of a wide variety of projects - from the development of compact biosensors to the creation of quantum computers. By the way, we regularly inform about quantum computers on the page of our portal. Subscribe to us not to miss the most important!

Scientists at Rensselaer Polytechnic Instituterealized that when nickel is "rolled" to the size of an extremely thin single-chip nanowire and exposed to mechanical energy, a very strong magnetic field is generated. This phenomenon is called magnetostriction. Conversely, if a magnetic field is applied to this material, then the atoms inside will change shape. This movement of atoms can be used to collect energy. Although nickel is a fairly common material, similar properties were not previously known.

Imagine creating a system with a hugethe number of nanowires. You can put it in an external magnetic field, and it will collect a very large amount of energy, but the system itself will be very small in comparison with the existing ones. - says Professor Fochtung.

Researchers have discovered this unique property.using a method called lensless microscopy, in which a synchrotron is used to collect data. A synchrotron is an installation with a circular vacuum chamber in which particles are accelerated to a speed close to the speed of light, and powerful electromagnets standing in their way determine the trajectory of their movement. Thus, one can learn a lot about the behavior and properties of elementary particles. But the amount of information collected from the synchrotron is very huge, and here machine learning algorithms come in handy.

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The data was fed to computer algorithms,which created three-dimensional images of electron density and displacement of nickel atoms in substances of different thickness and density. Using an array of neural networks that worked with big data, it was possible to obtain images of better quality than using traditional microscopes, giving researchers more information.

This approach reveals extremely smallobjects and learning about materials that we never knew, ”said Professor Fochtung. If you use microscope lenses, there is a limit to what you can see. This is determined by the size of the lens, its curvature and other characteristics. Now we do not have this limit.

Scientists believe that this approach to the studysubstances will allow researchers to learn even more about solid-state materials, such as those used in technological devices. This may even provide a deeper understanding of the work of human tissue and cells, which can also be studied using a new technique.