Predicting material properties with machine learning
Published in the Science and Technology of Advanced Materials Journal under the title “Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning”, the new approach could speed up the development of new materials with particular electronic or magnetic properties.
Today, most scientists use high throughput experimentation, involving large numbers of parallel experiments, to quickly map the relationships between the compositions, structures, and properties of materials made from varying quantities of the same elements. This helps accelerate new material development, but usually requires expensive equipment. High throughput calculation, on the other hand, uses computational models to determine a material’s properties based on its electron density, a measure of the probability of an electron occupying an extremely small amount of space. It is faster and cheaper than the physical experiments but much less accurate.
Materials informatics expert Yuma Iwasaki of the Central Research Laboratories of NEC Corporation, together with colleagues in Japan, combined the two high-throughput methods, taking the best of both worlds, and paired them with machine learning to streamline the process. They tested their approach using a 100 nanometre-thin film made of iron, cobalt and nickel spread on a sapphire substrate. Various possible combinations of the three elements were distributed along the film. These ‘composition spread samples’ are used to test many similar materials in a single sample.
The team first conducted a simple high throughput technique on the sample called combinatorial X-ray diffraction. The resulting X-ray diffraction curves provide detailed information about the crystallographic structure, chemical composition, and physical properties of the sample.
The team then used machine learning to break down this data into individual X-ray diffraction curves for every combination of the three elements. High throughput calculations helped define the magnetic properties of each combination. Finally, calculations were performed to reduce the difference between the experimental and calculation data.
Their approach allowed them to successfully map the ‘Kerr rotation’ of the iron, cobalt, and nickel composition spread, representing the changes that happen to light as it is reflected from its magnetized surface. This property is important for a variety of applications in photonics and semiconductor devices.
The researchers say their approach could still be improved but that, as it stands, it enables mapping the magnetic moments of composition spreads without the need to resort to more difficult and expensive high throughput experiments.
NEC Corporation – www.nec.com
Related article:
MIT researchers let AI identify materials’ best strain options
If you enjoyed this article, you will like the following ones: don't miss them by subscribing to :
