Using machine-learning tools, researchers were able to identify important features for characterizing materials that exhibit a metal-insulator transition. Image: Northwestern University and the Massachusetts Institute of Technology.An interdisciplinary team of researchers from Northwestern Engineering and the Massachusetts Institute of Technology has used artificial intelligence (AI) techniques to build new, free and easy-to-use tools for accelerating the rate of discovery and study of materials that exhibit a metal-insulator transition (MIT). These novel tools, reported in a paper in Chemistry of Materials, can also identify new features to describe this class of materials.
One of the keys to making microelectronic devices faster and more energy efficient, as well as designing new computer architectures, is discovering new materials with tunable electronic properties. The electrical resistivity of MITs can exhibit metallic or insulating electronic behavior, depending on the properties of the environment.
Although some materials that exhibit MITs have already been implemented in electronic devices, fewer than 70 materials with this property are known and even fewer exhibit the performance necessary for integration into new electronic devices. MITs switch electrically due to a variety of mechanisms, which makes obtaining a general understanding of this class of materials difficult.
“By providing a database, online classifier and new set of features, our work opens new pathways to the understanding and discovery in this class of materials,” said James Rondinelli, professor in materials and manufacturing at Northwestern University's McCormick School of Engineering and the paper’s corresponding primary investigator. “Further, this work can be used by other scientists and applied to other material classes to accelerate the discovery and understanding of other classes of quantum materials.”
“One of the key elements of our tools and models is that they are accessible to a wide audience; scientists and engineers don’t need to understand machine learning to use them, just as one doesn’t need a deep understanding of search algorithms to navigate the internet,” said Alexandru Georgescu, postdoctoral researcher in the Rondinelli lab, who is the paper’s first co-author.
Daniel Apley, professor of industrial engineering and management sciences at Northwestern Engineering, was a co-primary investigator, as was Elsa Olivetti, associate professor in materials science and engineering at the Massachusetts Institute of Technology.
Using their existing knowledge of MIT materials, combined with Natural Language Processing (NLP), the researchers scoured existing literature to identify the 60 known MIT compounds, as well as 300 materials that have a similar chemical composition but do not display an MIT. They have provided the resulting materials – as well as the features they identified as relevant – to scientists as a freely available database for public use.
Using machine-learning tools, the researchers then identified important features for characterizing these materials. They confirmed the importance of certain features, such as the distances between transition metal ions or the electrostatic repulsion between some of them, as well as the accuracy of the model. They also identified new, previously underappreciated features, such as how different in size from each other the atoms are or measures of how ionic or covalent the inter-atomic bonds are. These features were found to be critical for developing a reliable machine-learning model for MIT materials, which the scientists packaged into an openly accessible format.
“This free tool allows anyone to quickly obtain probabilistic estimates on whether the material they are studying is a metal, insulator or a metal-insulator transition compound,” Apley said.
This story is adapted from material from Northwestern Engineering, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.