Accelerated Design, Discovery, and Deployment of Electronic Phase Transitions (ADEPT)

Project Personnel

Christopher Hinkle

Principal Investigator

University of Notre Dame

Email

Gabriela Cruz Thompson

Co-PI

Intel

Suman Datta

Co-PI

Georgia Institute of Technology

Harsono Simka

Co-PI

Samsung

Albert Davydov

Co-PI

National Institute of Standards and Technology

Wei Chen

Co-PI

Northwestern University

Email

James Rondinelli

Co-PI

Northwestern University

Email

Divine Kumah

Co-PI

Duke University

Steve Kramer

Co-PI

Micron Technology

Funding Divisions

Division of Materials Research (DMR), Technology, Innovation and Partnerships (TIP)

The world has seen an enormous increase in global connectivity, information processing, and information storage driven by advances in technologies that rely largely on traditional semiconductors. Their underlying material platforms, however, are facing enormous challenges. A future generation of electronic devices can be established using materials which exist in multiple electronic states. Materials and devices that can be switched from an insulator to a metal by an external trigger would revitalize the U.S. semiconductor ecosystem, providing new paths to low-power computing systems and integration into systems for 6G and beyond applications. The project goal is to design and discover materials exhibiting such insulator-to-metal transitions (IMT) that enable room-temperature operation and display large changes in electrical resistivity. The research team, which comprises interdisciplinary expertise in computational and experimental materials physics, data science, and device engineering, aims to enable a culture shift in materials research, development, and deployment through training a well-equipped and diverse workforce with proficiencies in data-driven discovery of advanced materials. Leveraging Materials Genome Initiative principles, the team will deliver a tightly integrated codesign methodology to facilitate modeling and synthesis of new IMT materials with superior properties, and ultimately guide the design towards record-setting device performance to strengthen American leadership in future computing, storage and communication technologies and industries.

Publications

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network
H. Zhang, T. Lai, J. Chen, A. Manthiram, J. M. Rondinelli, and W. Chen
6/12/2024

Research Highlights

Learning Molecular Mixture Property using Chemistry-Aware Graph Neural Network
James Rondinelli and Wei Cheng (Northwestern University)
11/13/2024

Designing Materials to Revolutionize and Engineer our Future (DMREF)