Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International



Home > Press > 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

Using machine-learning tools, the scientists identified important features to characterize materials that exhibit a metal-insulator transition.

CREDIT
Northwestern University and the Massachusetts Institute of Technology
Using machine-learning tools, the scientists identified important features to characterize materials that exhibit a metal-insulator transition. CREDIT Northwestern University and the Massachusetts Institute of Technology

Abstract:
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.

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

Evanston, IL | Posted on July 30th, 2021

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.

揃y 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抯 corresponding primary investigator. 揊urther, 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.?br>
揙ne of the key elements of our tools and models is that they are accessible to a wide audience; scientists and engineers don抰 need to understand machine learning to use them, just as one doesn抰 need a deep understanding of search algorithms to navigate the internet,?said Alexandru Georgescu, postdoctoral researcher in the Rondinelli lab who is the study抯 first co-author.

The team presented its research in the paper 揇atabase, Features, and Machine Learning Model to Identify Thermally Driven Metal朓nsulator 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抯 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.

揟his 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抯 (ARPA-E) DIFFERENTIATE program, which seeks to use emerging AI technologies to tackle major energy and environmental challenges.

####

For more information, please click here

Contacts:
Brian Sandalow

Office: 847-467-3335

Copyright © Northwestern University

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

DOI:

Related News Press

News and information

Verizon and Zurich Instruments join Q-NEXT national quantum science center August 6th, 2021

Mixing a cocktail of topology and magnetism for future electronics: Joining topological insulators with magnetic materials for energy-efficient electronics August 6th, 2021

Controlling chaos in liquid crystals, gaining precision in autonomous technologies August 6th, 2021

NIST抯 quantum crystal could be a new dark matter sensor Peer-Reviewed Publication August 6th, 2021

Possible Futures

Quantum computing enables unprecedented materials science simulations: Multi-institutional team provides a foundation for unraveling the mysteries of magnetic materials August 6th, 2021

Verizon and Zurich Instruments join Q-NEXT national quantum science center August 6th, 2021

Mixing a cocktail of topology and magnetism for future electronics: Joining topological insulators with magnetic materials for energy-efficient electronics August 6th, 2021

Controlling chaos in liquid crystals, gaining precision in autonomous technologies August 6th, 2021

Chip Technology

Astonishing diversity: Semiconductor nanoparticles form numerous structures August 6th, 2021

Mixing a cocktail of topology and magnetism for future electronics: Joining topological insulators with magnetic materials for energy-efficient electronics August 6th, 2021

Non-linear effects in coupled optical microcavities August 5th, 2021

UVA Engineering researchers join quest to demonstrate photonic systems-on-chip: Future applications include faster, more efficient data centers and next-generation millimeter-wave wireless communication July 30th, 2021

Discoveries

A universal intercalation strategy for high-stable perovskite photovoltaics: Researchers at Kanazawa University demonstrate that the use of CsI intercalation technology greatly passivate defects, subsequently improve device performance. This technology may encourage a more widesp August 6th, 2021

Astonishing diversity: Semiconductor nanoparticles form numerous structures August 6th, 2021

Mixing a cocktail of topology and magnetism for future electronics: Joining topological insulators with magnetic materials for energy-efficient electronics August 6th, 2021

NIST抯 quantum crystal could be a new dark matter sensor Peer-Reviewed Publication August 6th, 2021

Materials/Metamaterials

Quantum computing enables unprecedented materials science simulations: Multi-institutional team provides a foundation for unraveling the mysteries of magnetic materials August 6th, 2021

Controlling chaos in liquid crystals, gaining precision in autonomous technologies August 6th, 2021

Water as a metal July 30th, 2021

Chaotic electrons heed 憀imit?in strange metals July 30th, 2021

Announcements

Verizon and Zurich Instruments join Q-NEXT national quantum science center August 6th, 2021

Mixing a cocktail of topology and magnetism for future electronics: Joining topological insulators with magnetic materials for energy-efficient electronics August 6th, 2021

Controlling chaos in liquid crystals, gaining precision in autonomous technologies August 6th, 2021

NIST抯 quantum crystal could be a new dark matter sensor Peer-Reviewed Publication August 6th, 2021

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters

A universal intercalation strategy for high-stable perovskite photovoltaics: Researchers at Kanazawa University demonstrate that the use of CsI intercalation technology greatly passivate defects, subsequently improve device performance. This technology may encourage a more widesp August 6th, 2021

Astonishing diversity: Semiconductor nanoparticles form numerous structures August 6th, 2021

Quantum computing enables unprecedented materials science simulations: Multi-institutional team provides a foundation for unraveling the mysteries of magnetic materials August 6th, 2021

NIST抯 quantum crystal could be a new dark matter sensor Peer-Reviewed Publication August 6th, 2021

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project









风韵多水的老熟妇_无码免费不卡AV手机在线观看_大黄网站