Scientists release new AI-based tools to accelerate functional
electronic materials discovery
The work could allow scientists to accelerate the discovery of materials showing a metal-insulator transition

Date:
July 29, 2021
Source:
Northwestern University
Summary:
The interdisciplinary team's work could allow scientists to
accelerate the rate of discovery and study of materials that
exhibit a metal- insulator transition.



FULL STORY ==========================================================================
An interdisciplinary team of scientists from Northwestern Engineering and
the Massachusetts Institute of Technology has used artificial intelligence
(AI) techniques to build new, free, and easy-to-use tools that allow
scientists to accelerate the rate of discovery and study of materials
that exhibit a metal- insulator transition (MIT), as well as identify
new features that can 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
the discovery of new materials with tunable electronic properties. The electrical resistivity of MITs may 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, only fewer than 70 with this property are known,
and even fewer exhibit the performance necessary for integration into new electronic devices. Further, these materials 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, Morris E. Fine Professor in Materials and Manufacturing at the McCormick School of Engineering and
the study'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 study's first co-author.

The team presented its research in the paper "Database, Features,
and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds," published July 6 in the academic journal Chemistry
of Materials.

Daniel Apley, professor of industrial engineering and management sciences
at Northwestern Engineering, was a co-primary investigator. Elsa
A. Olivetti, Esther and Harold E. Edgerton Associate Professor in
Materials Science and Engineering at the Massachusetts Institute of
Technology, was also a co-primary investigator.

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
are similar in chemical composition but do not show an MIT. The team has provided the resulting materials -- as well as features it's identified as relevant -- to scientists as a freely available database for public use.

Then using machine-learning tools, the scientists identified important
features to characterize 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 known, as
well as the accuracy of the model. They also identified new, previously underappreciated features, such as how different the atoms are in size
from each other, or measures of how ionic or covalent the inter-atomic
bonds are. These features were found to be critical in developing
a reliable machine learning model for MIT materials, which has been
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.

Work on this study was born from projects within the Predictive Science
and Engineering Design (PS&ED) interdisciplinary cluster program sponsored
by The Graduate School at Northwestern. The study was also supported
by funding from the Designing Materials to Revolutionize and Engineer
our Future (DMREF) program of the National Science Foundation and the
Advanced Research Projects Agency -- Energy's (ARPA-E) DIFFERENTIATE
program, which seeks to use emerging AI technologies to tackle major
energy and environmental challenges.

========================================================================== Story Source: Materials provided by Northwestern_University. Original
written by Brian Sandalow. Note: Content may be edited for style and
length.


========================================================================== Journal Reference:
1. Alexandru B. Georgescu, Peiwen Ren, Aubrey R. Toland, Shengtong
Zhang,
Kyle D. Miller, Daniel W. Apley, Elsa A. Olivetti, Nicholas Wagner,
James M. Rondinelli. Database, Features, and Machine Learning
Model to Identify Thermally Driven Metal-Insulator Transition
Compounds. Chemistry of Materials, 2021; 33 (14): 5591 DOI:
10.1021/acs.chemmater.1c00905 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210729122102.htm

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