Artificial Intelligence Meets Materials Science


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A Texas A&M University College of Engineering research team is harnessing the power of machine learning, data science and the domain knowledge of experts to autonomously discover new materials.

The team developed and demonstrated an autonomous and efficient framework capable of optimally exploring a materials design space (the materials design space is an abstraction of the concrete world. It is the space of all the possible materials under study, characterized by fundamental material features).

An autonomous system — or artificial intelligence (AI) agent — is defined as any system capable of building an internal representation, or model, of the problem of interest, and that then uses the model to make decisions and take actions independent of human involvement.

The authors of this interdisciplinary work are Dr. Anjana Talapatra and Dr. Raymundo Arroyave from the Department of Materials Science and Engineering, and Shahin Boluki, Dr. Xiaoning Qian and Dr. Edward Dougherty from the Department Electrical and Computer Engineering.

An engineering research team harnesses the power of machine learning and artificial intelligence to create software that autonomously discovers new materials. | Image: Dharmesh Patel

Their autonomous framework is capable of adaptively picking the best machine learning models to find the optimal material to fit any given criteria. Their research, funded by the National Science Foundation and the Air Force Office of Scientific Research, will reduce the time and cost spent going from lab to market by ensuring the greatest possible efficiency in the search for the right material.

The underlying mathematical theory has many applications, including affecting the field of biomedicine. For example, with their Bayesian learning and experiment design framework, a disease can be modeled to uncover critical risk factors to develop effective therapeutics for specific patients and reduce the cost of human clinical trials.

Advanced materials are essential to economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security and human welfare, yet it can take 20 or more years to move a material after initial discovery to the market.

Materials Genome Initiative

The team wanted to test the framework exhaustively, so they carried out the demonstration in a closed-loop computational platform, using quantum mechanics to predict properties of MAX-phases, which are promising materials for high-temperature applications, including novel oxidation resistant coatings for jet engine turbine blades.  The Texas A&M group is also applying the framework to the discovery of high-temperature shape memory alloys that can be used to build aerospace vehicles with morphing wings, for example.

Autonomous Innovation

Significant research on efficient experiment design techniques has been done before. However, this team is the first to use a Bayesian based technique (meaning they take stock of all that is known about a material/material class and leverage that knowledge to find the best material) and employ it in an autonomous fashion, continuously searching not only for the next best computation/experiment to run but also for the best model to represent the acquired data.

“The accelerated exploration of the materials space to identify configurations with optimal properties is an ongoing challenge,” said Talapatra, who works as a computational scientist in Arroyave’s Computational Materials laboratory. “Current paradigms are centered around the idea of performing this exploration through high-throughput experimentation and/or computation. Those approaches do not account for the constraints in resources available. We have addressed this problem by framing materials discovery as an optimal experiment design.”

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