The role of AI in modern material science

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Advances and Impact of AI in Material Science

Materials science is a multidisciplinary field with applications across various areas of science and engineering. Material design and discovery have been chiefly a repetitive process of trial-and-error that requires considerable human effort, time, and resources, even when supplemented by computer simulations.

New technologies, however, bring fresh ways of working, and engineering is no exception. In this diverse field, traditional, human-intensive work is replaced by AI-based simulation and automated experimentation for complex problem-solving and faster, more accurate outcomes.

How does AI enhance material science?

The complex relationships between different phases of materials and the structures they form are hard to predict using traditional theoretical and experimentation approaches. This is especially true for discovering more complex materials required for technological advancement and sustainability.

AI solves these problems by automatically extracting the thermodynamic and microstructural features in datasets generated by electronic, atomistic, and continuum simulations. Machine learning models, coupled with big batches of data and active learning cycles, lead to high-quality predictions and help researchers understand the complex hidden properties of materials.

AI and physics-based simulation work together to speed up material discovery and design. In doing so, materials can be made with various properties and structures tailored to meet particular requirements.

Has the viability of this AI-driven method been tested in real life?

AI-driven material science is a fast-growing interdisciplinary field that has several unexplored opportunities. Bringing AI, supercomputing, and experimental data together streamlines new material creation. Google scientists recently discovered 380,000 new materials using their deep learning AI and Graph Networks for Materials Exploration tool or GNoME, and published this discovery in the journal Nature. Of this, 736 have been tested physically, proving this method’s viability in real life.

What’s next in AI-powered Material Science?

The predictive capabilities of AI depend on the availability of large data sets. As of now, we need to find new ways to enhance AI capabilities in material science. Employing active learning cycles is one way to go about it.

This adaptive learning method allows the algorithm to determine the most informative data points through some pre-defined metric and pass them to a human labeler to add them to the training set. Only the development of AI/ML algorithms, high-quality material datasets, and robust computing resources will enable us to discover compositionally complex alloys, and much more.

Find out how mechanical engineers at the University of California used artificial neural networks (ANNs) to create a living, adaptive material that they call a mechanical neural network (MNN), an innovative intersection of AI and material science.

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