Machine learning helps predict new materials for nano alloys, semiconductors, rare earths

New Delhi : Scientists have used machine learning to develop a design map of alloys at the nano-scale which can help predict the match of pairs of metals that can form bimetallic nano-alloys, a new frontier in the quest of new materials having applications in biomedicine and other areas.
These nano-alloys, also called core-shell nano-cluster alloys, are such wherein one metal forms the core and another stays on the surface as a shell. It is important to know under what conditions core-shell structures are formed in the nano-cluster alloys and which metal forms the core, and which stays on the surface as a shell.
The 95 metals from the periodic table can potentially form 4,465 pairs. It is experimentally impossible to determine how they behave in forming nano-cluster alloys. But computers can be programmed to predict the behaviour of these pairs and more through “machine learning” (ML) – here the machine is taught to recognise patterns by feeding in a number of patterns with well-defined attributes.
However, scientists faced a stumbling block here because of the limited number of experimentally synthesized binary nano-clusters with clear identification of the chemical ordering of constituents, and few core shell combinations studied theoretically. ML could not be applied with confidence on small data set of sizes less than or around 100.
Also Read Premium Bose QC 45 suits wide range of audio content However, researchers at the S.N. Bose Centre for Basic Sciences, an autonomous institute of the Department of Science and Technology, 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.
In their latest paper published in the Journal of Physical Chemistry, they investigated the key attributes driving the core shell morphology using the statistical tool of machine learning applied on this large data set.