PCL-test bed: aurora (autonomous utah resource for open research automation): a utah programmable cloud laboratory node for quantum materials and beyond

The National Science Foundation awarded the University of Utah $5 million through a Cooperative Agreement on August 1, 2026, to design and deploy AURORA (Autonomous Utah Resource for Open Research Automation), a cloud-accessible laboratory platform for remote experimentation in quantum materials and two-dimensional materials research.

AURORA will automate the assembly and characterization of 2D materials by integrating robotics and artificial intelligence into what has traditionally been a manual, human-intensive process. The platform will standardize experiments, improve reproducibility, and accelerate materials discovery while enabling remote access for researchers across universities, industry, and government laboratories through a cloud interface. The project includes workforce development components comprising workshops, online educational content, and hands-on research training in robotics, artificial intelligence, cloud experimentation, and advanced manufacturing.

The award is funded under NSF's Technology, Innovation, and Partnerships program (CFDA 47.084) and carries a period of performance from August 1, 2026, through July 31, 2030. Work is performed in Salt Lake City, Utah.

Abstract

PCL-test bed: aurora (autonomous utah resource for open research automation): a utah programmable cloud laboratory node for quantum materials and beyond aurora - autonomous utah resource for open research automation will create a new kind of scientific laboratory where researchers can remotely design, run, and analyze experiments through the cloud. Similar to how cloud computing transformed access to powerful computers, aurora will make advanced experimental facilities more accessible, programmable, and reproducible. Aurora will allow researchers from universities, industry, and government laboratories to remotely access sophisticated experiments that are normally available only to a small number of specialized facilities. While most materials exist in bulk (3D) form, one can imagine materials so thin that their structure approaches a 2-dimensional form. These so-called ?2D materials? Have unexpected and useful properties for a wide variety of applications ranging from telecommunication to electronics and beyond. However, while 2D materials have traditionally been assembled manually, by humans physically stacking layers together, there is now a new opportunity to improve and accelerate this task by leveraging robotics and artificial intelligence. By standardizing and automating these experiments, aurora will improve precision, enhance reproducibility, and accelerate discovery of 2D materials. The project will also train students and researchers in robotics, artificial intelligence, cloud experimentation, and advanced manufacturing through workshops, online educational content, and hands-on research experiences. Aurora will train and strengthen the U.S. Scientific workforce while advancing national leadership in autonomous, data-driven research infrastructure and next-generation manufacturing technologies. Aurora will build upon two prior NSF investments, VIZ., monark, a factory of the future for robotic assembly of 2D materials and cloudlab, a platform for accessing remote infrastructure. The result is a first of its kind AI-enabled self-driving laboratory for constructing 2D heterostructures where users are able to leverage AI to design as well as actualize experiments. Aurora includes laboratory experiment stages for exfoliation, optical characterization, stacking, device fabrication, cryogenic transport measurements, and archival in a unified closed-loop platform that learns from each new experiment. The project technical objectives include, first, expanding automation across the full experimental workflow for van DER waals heterostructures, enabling reproducible fabrication and measurement of quantum materials devices with minimal human intervention. Second, implementing a ?fork?validate?merge? Paradigm for laboratory development, adapting concepts from software version control to experimental hardware and robotic workflows. This framework allows new instrumentation, control software, and artificial intelligence models to be safely tested, validated, and deployed across distributed laboratory nodes. Third, integrating artificial intelligence throughout the experimental pipeline, including computer vision for sample identification along with physics-informed descriptor extraction, anomaly detection, adaptive workflow optimization, and large-language-model-assisted scientific reasoning and hypothesis generation. Project science drivers include study of twisted bilayer graphene, quantum anomalous hall heterostructures, and transition-metal dichalcogenides. While these materials are at the frontier of scientific breakthroughs, advances in this area will only be impactful if they are reproducible by other scientists. Aurora strives to establish successful prototypes for future automated laboratories by prioritizing reproducibility, accessibility, and accelerated discovery. Aurora will enable ground-breaking applications in domains such as next-generation semiconductors, ultra-low power electronics, scalable quantum computing, and miniaturized on-chip communications. 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.- subawards are planned for this award.