Can AI Help People Design Better Materials?

Can AI Help People Design Better Materials?

Emily Newton 12/04/2022
Can AI Help People Design Better Materials?

AI is completely transforming materials science.

The literal fabric of the modern world is the complex, uniquely designed creations that make up phones, computers, cars and countless other vital items. AI is helping materials scientists make these items stronger, more affordable and more effective for next-gen items. 

Research that might have taken years or even decades to complete can now be conducted in a matter of hours. The development of these new materials with the help of AI could help usher in a new era of cutting-edge metamaterials that could change technology forever. Here’s what people can expect in the not-so-distant future with the help of artificial intelligence.

Cloud Computing and Workflows

Cloud computing resources have made AI accessible to virtually any materials science research team today. It makes it much more affordable since computing resources can be outsourced. Materials scientists can use AI to improve research processes and workflows with this tool in hand. For example, it can autonomously monitor performance for software or test experiments. 

It can also help evaluate research data for points of interest and patterns. This significantly speeds up the research process by cutting down the time it takes to pinpoint the most valuable data from a study or experiment. AI will also often extract insights about data that human researchers may not have noticed. Its pattern recognition capabilities allow it to analyse information completely differently from how a human might see things. 

Materials scientists are also using AI to analyse research and workflows used by other teams. A certain process or technique that works well in one project will often be effective in others. AI helps identify these by recognising similarities between many different workflows and tasks, speeding up the materials science research process. 

Developing Metamaterials, Surfaces and Coatings

Metamaterials are widely considered to be the future of materials science, but they are extremely challenging to create. They are designed to manipulate physical properties, such as light or sound, in ways things aren’t naturally capable of. They have applications in countless fields, like aerospace, optics, medicine and electronics. AI is helping researchers develop metamaterials as well as metasurfaces and advanced materials coatings. 

For example, a leading researcher and professor at MIT is using a deep learning algorithm to create metasurfaces, an ultra-thin film that helps stabilise metamaterials. The algorithm can process complex irregular shapes more effectively than humans and identify connections between certain forms and properties. It can rapidly analyse combinations of materials and functions to identify those that show potential and those that would not work. This allows researchers to focus on as many potentially useful combinations as possible while spending less time searching for them. 

The same methodology could be applied to materials science at large. Advanced materials and coatings can improve durability and functionality in electronics and other objects even beyond metamaterials and experimental technologies. Using AI to help develop these materials and coatings allows researchers to improve and discover new designs faster and more effectively. 

Discovering New Materials

AI isn’t just helping scientists analyse data or improve materials. It is even discovering new materials altogether. This is especially important in developing highly complex metamaterials. 

Materials scientists usually start with an application the material needs to fulfil, but the avenues they could test are virtually endless. Lab processes such as spectroscopy or computer modelling have often been used for materials discovery research in the past, but AI is unlocking a whole new level of efficiency. 

For instance, a research team at MIT discovered a set of new materials for energy storage out of an original list of 3 million potential options. They used a neural network to accomplish this in only five weeks when it otherwise could have taken 50 years with conventional processes. The AI was able to comb through the millions of potential materials and predict the properties of each one, pulling out the options with the best possible set of potential properties. This resulted in eight newly discovered items the researchers could focus on. 

Similarly, a team of researchers at Cornell University have created the Scientific Autonomous Reasoning Agent, or SARA. The team explained how the AI model could be used to drastically cut down the time, money and resources needed to discover and understand new materials. They used it specifically for researching metastable inorganic materials. SARA analysed the properties of these materials, such as phase and temperature, in seconds. 

AI and Next-Gen Materials Science

Materials science has become more important over the decades, especially as technology has begun to reach the limits of things that exist today. Fresh materials capable of achieving new feats are needed to continue driving innovation in countless fields. Only with the help of AI can materials scientists discover, research and develop these items rapidly and make the most of them sooner than ever though possible.

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Emily Newton

Science & Tech Expert

Emily Newton is the Editor-in-Chief of Revolutionized. She is a science and technology journalist with over three years covering industry trends and research. 

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