AI and machine learning can analyze data sets to provide combinations for new composite materials

Materials science has been using a conventional laboratory process to identify and discover new composite materials from scratch. Days-long experimentation with different components and a lot of research went into making new materials. The emergence of artificial intelligence has impacted the discovery of new materials like metallic glass. An article published in Science Advances talks about the accelerated discovery of metallic glasses through machine learning and high throughput experiments.

In the article, the scientists say, "This paper illustrates how ML and HiTp experimentation can be used in an iterative/feedback loop to easily accelerate discoveries of new MG systems by more than two orders of magnitude as compared to traditional search approaches relied upon for the last 50 years."

AI algorithms can predict the components from the existing database and repetitive analysis to provide new recipes or combinations for making new materials. Machine learning systems can be used in mining data from research materials and journals to extract names or sentences related to material discovery, combine them, and provide insights into new combinations of materials. The MIT report mentions that a team of researchers at MIT, the University of Massachusetts, and the University of California aspires to close the materials-science automation gap, with a new artificial intelligence system that would pore through research papers to deduce 'recipes' for producing particular materials.

These machine learning systems use supervised, unsupervised, and semi-supervised algorithms to arrive at conclusions. The supervised algorithm will be fed with a trained dataset that is used to establish relations whereas the unsupervised algorithm will not have any trained data sets and they are left to discover interesting data structures.

Using AI and machine learning in the discovery of materials can create new alloys at a much faster pace and address the issue of limited composite material resources like steel. An article in The Verge quotes Chris Wolverton, a materials scientist at Northwestern University who says, "We do quantum mechanical-level calculations of materials, calculations sophisticated enough that we can actually predict the properties of a possible new material on a computer before it's ever made in a laboratory."

Material.ai is a platform that offers to discover sustainable and dependable materials leveraging AI that can serve as good alternatives to the world's resources.

A scenario where scientists can input the data containing the properties of existing materials into the AI systems and gain results for new materials is the new way of performing scientific experiments. These AI algorithms use virtual calculations and computations instead of performing physical experiments. Later, scientists can use the instructions provided by the system to create new composite materials.

A paper published by Cambridge University reviews and discusses recent applications of using machine learning in predicting mechanical properties of composite materials and also the role of ML in designing composite materials with desired properties. The wide range of applications of AI and capabilities of machine learning algorithms to analyze huge chunks of data will aid in more nascent discoveries in the field of science.