The world has seen an enormous increase in computing power, but the current path forward for the semiconductor industry is beset with roadblocks. A different strategy for a future generation of electronic devices is based on materials that exist in multiple electronic states. A new generation of electronic materials are required for this purpose, as are the means for switching between multiple electronic states. The fundamental science that is the focus of this project is centered on the sudden change in the electrical properties of certain materials when they are switched through a so-called metal-to-insulator transition by an external trigger. On one side of the transition, the material behaves like copper metal, while on the other side, it behaves like insulating wood. The project goal is to design and discover materials exhibiting such metal-to-insulator transitions that enable room-temperature operation and that display large changes in the key property of interest; the electrical resistivity. The strategy is to control properties by structural design at the atomic scale. The approach employs a tightly integrated combination of experiment, theory, and data-mining of the literature, that would enable new insights to emerge and aid in the design of desirable materials. This project will deliver a research workflow with a suite of tools to enable assessment and experimental validation of new concepts for the discovery of key materials. The project will articulate protocols for selecting high-performing materials, leading to an expanded palette of compounds that could impact future technologies. The teaching and training of students and the discovery capabilities of the project are interwoven, and aimed at broadening participation through the involvement of the investigators and their group members in public outreach events. The development of modules for undergraduate and graduate courses and the involvement of students in interdisciplinary team environments are intrinsic to project plan. The project will yield a plethora of new and mined data on a range of oxides and new computational materials approaches. These will be aggregated into open-access databases on public portals.
This project will pursue discovery of the atomic-level genetic code of materials displaying metal-to-insulator transitions through approaches that establish links between unit cell level crystal structure and the macroscopic electronic response, profiting from a coupling of theory, data, and comprehensive experimentation. At the present time, the essential data and structure-electronic function relationships to decipher the genetic code (generic descriptors) of metal-to-insulator transitions do not exist in a format which permits predictive synthesis. The project?s significance is that it recasts the problem into one of atomic structure, focusing on the role of different kinds of structural distortions, notably, breathing modes, Jahn-Teller distortions, and Peierls-like instabilities across a broad range of structure types and chemistries. The project will generate and collect a range of data that will permit the mapping of electronic interactions into atomic features, applying informatics-based methods to enable supervised and unsupervised learning. The project will articulate predictive rules and protocols for selecting high-performing materials, leading to an expanded palette of compounds that could impact technologies beyond electronics. The teaching and training of students at multiple levels and the discovery capabilities of the project are interwoven and aimed at broadening participation by through public outreach events, through the development of modules for undergraduate and graduate courses; and finally, by involving students in interdisciplinary team environments. The project will yield a plethora of new data on a range of oxides and new computational materials approaches. These will be aggregated into databases on public web-portals using a new portable file format designed for materials data. New methods of data visualization will allow external users to interact, query, and analyze the data for aims beyond those proposed herein. Data-driven models and informatics workflows for generating quantitative models for metal-to-insulator performance will be hosted with the aforementioned data and visualization tools on the MIST: Metals and Insulators by Structural Tuning platform. The PIs also plan to release MIST as open source and build a user community around the platform by ensuring that interested researchers are able to contribute to the MIST codebase. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award. Advances in synthesis, theory, and characterization will strengthen the scientific capabilities and workforce by allowing students and academic or industrial researchers to employ the formulated structure-property relationships for educational and research purposes.
