NSF Award
2324175
Non-Technical Description:<br/>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. <br/><br/>Technical Description:<br/>The goal of the Accelerated Design, Discovery, and Deployment of Electronic Phase Transitions (ADEPT) project is to implement an accelerated discovery and codesign engine for efficient deployment of insulator-metal transition (IMT) materials traditionally marred by sparse prior data and system-level constraints. Achieving this goal requires moving beyond conventional, linear approaches to materials discovery, transforming them into a cyclic and iterative process. The project formulates new computational approaches that fuse computational data with high-throughput materials synthesis and characterization data to overcome key challenges of (i) Materials Discovery from Sparse & Expensive Data, (ii) Efficient Decoding of High-Dimensional Experimental Data, and (iii) Property-Performance Mismatch upon Integration. Their confluence hinders the advancement of novel material platforms for future microelectronic and wireless communication technologies. The project goal will be executed by creating integrated protocols that transform the standard sequential discovery steps (hypothesis generation, synthesis, characterization) into closed-loops fashioned to overcome these challenges: (1) AI-Aided Virtual Screening and Adaptive Discovery, (2) Accelerated Synthesis and Characterization Analytics, and (3) Materials Integration, Device Fabrication, and Codesign. Success with this framework will allow for the realization of material objectives within device constraints and deliver the following outcomes: (1) new classes of single and two-phase IMT materials (2) distributed in open-access databases, (3) theories of IMT behavior, (4) novel IMT thin film synthesis methods, (5) contactless characterization methods to improve throughput, (6) adaptive learning methods to achieve codesigned materials and devices, and (7) quantitative understanding of device performance to benefit future scalability and manufacturing with industrial partners.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
