- SFG FRC 2027 | Program starts from 19th Dec. 2026 | Admissions open for Entrance Test Click Here to know more →
- Prelims Test Series (PTS) for Prelims 2027 starts 11th Oct. 2026 Click Here to know more →
- [Pre-order] Ethics Redbook 3rd Edition: Delivery starts from 10th October onwards Click Here to Read More →
- 21 Sept. | GS Advance Program (GSAP) for UPSC 2027 Mains starts from 11th Oct. Click Here to Read More →
What is the News?
Scientists have used machine learning to develop a design map of alloys at the nanoscale which can help predict the match of pairs of metals that can form bimetallic nanoalloys.
What are Nanoalloys?
Nano alloys are those in which one metal forms the core and another stays on the surface like a shell. These are also called core-shell nanocluster alloys.
These are a new frontier in the quest of scientists for new materials and have applications in biomedicine and other areas.
What is the issue with Nanoalloys?
It is important to know under what conditions core-shell structures are formed in the nanocluster alloys and which metal forms the core and which stays on the surface like a shell.
A number of factors like cohesive energy difference, atomic radius difference, surface energy difference and electronegativity of the two atoms may play a part in the core and shell preference of the atoms.
For instance, the periodic table has 95 metals of different categories ranging from alkalis to alkaline earth which can potentially form 4465 pairs. It is experimentally impossible to determine how they behave in forming nanocluster alloys.
But machine learning can be programmed to predict the behaviour of these pairs. The machine is taught to recognise patterns by feeding in a number of patterns with well-defined attributes.
However, scientists faced a stumbling block as Machine Learning could not be applied with confidence on small data sets of sizes less than or around 100.
How was this problem solved by researchers?
Researchers circumvented this problem by calculating the Surface-to-core relative energy on a variety of possible binary combinations of alkali metals, alkaline earth, basic metals, transition metals and p-block metals to create a large data set of 903 binary combinations
Source: The post is based on the article “Machine learning helps predict new materials for nano alloys, semiconductors & rare earths” published in PIB on 31st May 2022.
