SUNY Polytechnic Institute is joining a $19.9 million National Science Foundation initiative designed to transform how advanced electronic and quantum materials are discovered, tested and manufactured. Led by Rice University, the four-year project will combine artificial intelligence, robotics, automated synthesis equipment and cloud-based laboratories to create a new generation of research infrastructure in which experiments can be planned, performed and refined with minimal human intervention. The initiative, known as “Revolutionizing AI-Driven Autonomous Experimentation for Next-Generation Semiconductor Synthesis,” or READINESS, is scheduled to begin on August 1 and will also include researchers from the University of Texas at Austin.
At the center of READINESS is an autonomous laboratory platform that links machine-learning systems to physical equipment capable of producing and analyzing materials. Instead of relying exclusively on scientists to select a composition, prepare a sample, run an experiment and interpret the results, the system will allow artificial intelligence to guide the entire cycle. Algorithms can evaluate previous measurements, identify promising experimental conditions and propose the next set of tests. Robotic systems then carry out those instructions, while advanced characterization tools measure the resulting material properties. The information is fed back into the software, enabling the platform to continuously improve its decisions.
The project will focus initially on materials with potentially major consequences for computing and communications, including two-dimensional materials, oxide semiconductors and diamond thin films. Two-dimensional materials are only a few atoms thick and can exhibit electrical, optical and mechanical properties that differ dramatically from those of their bulk counterparts. Oxide semiconductors can be useful in displays, sensors, power electronics and other devices, while diamond films may offer exceptional thermal conductivity and durability. By rapidly testing different synthesis conditions, researchers hope to identify materials that could support faster electronics, lower-power computing and emerging quantum technologies.
SUNY Poly will contribute specialized expertise in semiconductor materials processing, thin-film fabrication and workforce development. Dr. Michael Carpenter, the institute’s vice president for research and a co-principal investigator, will help lead development of an autonomous physical vapor deposition system for oxide thin-film synthesis. Physical vapor deposition, or PVD, creates thin coatings by vaporizing a source material inside a controlled chamber and depositing it onto a substrate. Parameters such as temperature, pressure, gas composition and deposition rate can strongly influence the final film’s structure and performance. Automating these variables will allow the research team to explore combinations that would be difficult, slow or expensive to test manually.
The institute will also oversee installation of an autonomous chemical vapor deposition system developed at Rice University by materials scientist and principal investigator Dr. Jun Lou. Chemical vapor deposition, or CVD, forms materials when gaseous chemical precursors react or decompose on a heated surface. It is widely used to manufacture semiconductor layers, carbon-based materials and other technologically important films. For two-dimensional materials, small changes in precursor flow, temperature and substrate conditions can determine whether a uniform atomic layer forms or whether defects, unwanted phases and irregular growth appear. An autonomous CVD platform can systematically map these conditions and use its findings to refine future experiments.
A defining feature of READINESS is that SUNY Poly and Rice University will operate identical autonomous PVD and CVD systems. Matching equipment at separate locations will create what researchers describe as a shared node for programmable experimentation. Scientists can compare results across laboratories, reproduce promising recipes and examine how small differences in equipment, environment or materials influence outcomes. This approach addresses a persistent challenge in materials science: a result that works in one laboratory may not transfer reliably to another. Standardized autonomous systems, connected through digital infrastructure, could improve reproducibility while allowing experiments to continue remotely.
The platform will also incorporate digital twin technology. A digital twin is a computational representation of a physical system that can simulate how equipment and materials are expected to behave. In the READINESS environment, such models could help predict the effects of changing process conditions before a real experiment is launched. Researchers might use a digital twin to estimate how a temperature shift could affect crystal growth, or how a change in gas flow might alter the thickness and defect density of a film. The simulations will not replace laboratory measurements, but they can help prioritize experiments, reduce wasted resources and make autonomous decision-making more efficient.
For SUNY Poly, the initiative is intended to advance research and prepare people for an evolving semiconductor industry. The institute will help create short courses and stackable credentials for students, engineers and industry professionals seeking skills in semiconductor manufacturing, laboratory automation and AI-enabled materials research. These credentials could provide flexible pathways for workers who need targeted technical training without pursuing a full degree. Participants may learn how to operate deposition equipment, interpret materials data, maintain robotic systems, manage cloud-connected laboratories or work with machine-learning tools that guide experimental processes.
The workforce component reflects a broader transformation taking place in scientific research. As laboratories become increasingly automated, future researchers will need expertise that crosses traditional boundaries between materials science, electrical engineering, computer science, robotics and data analysis. A scientist working with an autonomous laboratory may not personally perform every deposition or measurement, but will need to understand how the equipment works, how data are generated and how algorithms make recommendations. Dr. Winston Soboyejo, president of SUNY Poly, said the project demonstrates the institute’s growing role in semiconductor innovation, advanced manufacturing and applied artificial intelligence while strengthening the talent pipeline needed for the country’s technology sector.
READINESS is supported through the NSF’s Programmable Cloud Laboratories Test Bed initiative, a national effort to establish remotely accessible research facilities that use artificial intelligence and automation to make experimentation faster, more reliable and more reproducible. If successful, the model could change the pace of materials discovery by allowing researchers in different locations to share equipment, experimental protocols and real-time data through a common digital environment. Rather than waiting weeks or months to complete a sequence of experiments, scientists could use autonomous systems to run repeated tests around the clock, while machine-learning tools identify the most promising directions.
The project also illustrates why advanced materials research is becoming increasingly connected to national semiconductor strategy. Modern technologies depend on materials that can conduct, insulate, emit, detect or withstand extreme conditions with exceptional precision. Discovering those materials is often slow because the space of possible chemical compositions and manufacturing conditions is enormous. By combining automated synthesis with artificial intelligence, the READINESS team aims to search that space more intelligently and to move promising discoveries more quickly toward scalable manufacturing. Dr. Carpenter said the collaboration could accelerate both scientific discovery and industrial adoption, while creating new opportunities for students, researchers and companies.
For Dr. Lou, the partnership between Rice University and SUNY Poly represents a new approach to AI-enabled experimentation in which research institutions share not only ideas, but also compatible machines, data and training opportunities. The long-term ambition is to make advanced laboratories more accessible and to build a connected ecosystem in which materials can be designed, synthesized, analyzed and optimized across institutional boundaries. As autonomous laboratories become more capable, they could help researchers tackle some of the most difficult problems in electronics and quantum technology while giving the next generation of scientists hands-on experience with the tools likely to define the future of manufacturing.
Subject of Research: Artificial intelligence, autonomous laboratories, semiconductor materials, robotics, thin-film synthesis, quantum materials and workforce development
Article Title: SUNY Poly Joins $19.9 Million National Science Foundation Initiative to Accelerate AI-Driven Materials Discovery
News Publication Date: Tuesday, July 28, 2026
Web References: https://news.rice.edu/news/2026/accelerating-discovery-rice-receives-nearly-20m-nsf-award-ai-powered-materials-laboratory
Image Credits: SUNY Polytechnic Institute
Keywords
Artificial intelligence, materials discovery, autonomous experimentation, semiconductor manufacturing, robotics, cloud laboratories, physical vapor deposition, chemical vapor deposition, two-dimensional materials, oxide semiconductors, diamond thin films, quantum technologies, Rice University, SUNY Polytechnic Institute, National Science Foundation
Tags: AI-driven materials discoveryAI-guided experimentationautomated material testing and analysisautonomous laboratory platformscloud-based laboratory automationhigh-throughput experimental systemsinterdisciplinary collaboration in AI materials discoverymachine learning in materials sciencenext-generation semiconductor synthesisNSF-funded AI and robotics in researchquantum materials researchrobotic synthesis of advanced materials
