AI Accelerates Material Science Discoveries

AUSTIN, Texas - Aug. 15, 2024 - PRLog -- AI has helped uncover the structures of millions of proteins. Can it be used to identify useful materials as well?

Artificial intelligence and machine learning-based research efforts are accelerating scientific discovery across the board.

Among the premier examples is Google's DeepMind project, which has been able to predict the complex folding structures of over 214 million individual proteins – a feat that could not be accomplished so quickly without the assistance of AI/ML.

Now, attention is focusing on using AI-based methods to revolutionize the discovery of novel new materials in the hopes of solving some of the thorniest problems in industries ranging from high-tech electronics manufacturing to energy generation to environmental conservation and sustainability.

From a science perspective, the discovery of new material applications has some similarities with protein research. Both have incredibly small internal structures based on a limited number of elements (in the case of materials) or amino acid sequences (the building blocks of proteins) that are tricky to visualize even with high-tech microscopes – and like proteins with their changing amino acid sequences, even minor variations in the crystalline structures of elements in materials can produce results with vastly different properties.

Chemistry is the key to advanced, high-performance materials

If we want to identify (or create) a new material to meet a specific requirement, investigating its atomic structure is important. For example, carbon atoms can be arranged in different ways to produce dramatically different results: Carbon atoms can be formed into diamonds or the lead in pencils – or flattened into a single layer to produce slippery graphite.

But we aren't limited to a single element in our atomic structure. Many of the most commonly known materials result from mixing different elements together to produce alloys – for example, bronze is a combination of copper and tin, brass is made of copper and zinc, and steel is a combination of iron and carbon.

More sophisticated alloys, such as molybdenum steel, helped give the Model T Ford its renowned roadworthy toughness, while the strong yet lightweight aluminum alloy 2024 (made with small amounts of copper, manganese, and magnesium added to the aluminum) helped the Boeing 747 widebody jets take to the air.

How is AI able to accelerate identifying candidates that have the best chance of matching a given set of material property requirements/use cases?

In the current era, a significant effort has been directed at improving the composition of batteries for use in electric vehicle motors.

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