New Delhi, May 31, 2022:
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.
These nano alloys, also called core-shell nanocluster alloys, in which one metal forms the core and another stays on the surface as a shell, are a new frontier in the quest of scientists for new materials and have applications in biomedicine and other areas.
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 as 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.
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 computers can be programmed to predict the behaviour of these pairs and more through ‘machine learning’. The machine is taught to recognise patterns by feeding in a number of patterns with well-defined attributes. The more the data fed into the computer, the more accurate will be the recognition of an unknown data by the computer.
However, scientists faced a stumbling block here because of the limited number of experimentally synthesised binary nanoclusters with clear identification of the chemical ordering of constituents, and few core−shell combinations studied theoretically. Machine Learning could not be applied with confidence on small data set of sizes less than or around 100.
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 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. Core-shell structures with lighter metals having lower atomic numbers in the core were classified as Type 1, and those having the heavier metals in the core were classified as Type 2. A number of attributes were built to characterise each data point in the set. The performance of the ML model was tallied with existing experimental data, and the ML model was proved to be reliable.