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Machine Learning that predicts material properties after straining

machine learning

A machine learning approach has been developed that can predict changes to the properties of materials from straining. This was achieved by a group of scientists from Nanyang Technological University, Singapore, who collaborated with researchers from the Massachusetts Institute of Technology (MIT), US and the Skolkovo Institute of Science and Technology, Russia.

This is a big breakthrough as the research will help scientists engineer new materials with customized properties that can be used in communications, information processing, and energy fields.

In the Proceedings of the National Academy of Sciences earlier this week, scientists demonstrated use of Artificial Intelligence to identify the most energy-efficient strain pathways. This could transform diamond into a more effective semiconductor.

What is Strain Engineering?

When a semiconductor material is bent or strained, the atoms in its structure are perturbed. This changes its properties such as conduction of electricity, heat or the transmission of light. This process is called ‘strain engineering’.

Last year, NTU Singapore and MIT authors reported in Science, that in spite of being the hardest natural material on Earth, diamond nanoneedles could be bent and stretched as much as 9 per cent.

[Image Courtesy: Analytics India Magazine]

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