Artificial Intelligence, Deep Tech

$20M NSF grant funds AI-powered materials science at SUNY Poly

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August 19, 2026 by Ivy Delaney

A $19.9 million National Science Foundation grant will fund a four-year project beginning August 1, pairing SUNY Poly with Rice University and the University of Texas at Austin to accelerate materials discovery using artificial intelligence. The “Revolutionizing AI-Driven Autonomous Experimentation for Next-Generation Semiconductor Synthesis” (READINESS) initiative will see identical autonomous physical vapor deposition and chemical vapor deposition systems installed at both SUNY Poly and Rice University, scaling research capacity and collaborative training.

“This award reflects the growing recognition of SUNY Poly as a national leader in semiconductor innovation, advanced manufacturing, and applied artificial intelligence,” said SUNY Poly President Dr. Michael Carpenter, Vice President for Research, will co-lead development of an autonomous PVD system focused on oxide thin film synthesis.

SUNY Poly Develops Autonomous PVD and CVD Systems for Materials Synthesis

Dr. SUNY Poly will also contribute to workforce development by creating short courses and credentials in semiconductor manufacturing, laboratory automation, and AI-enabled materials research, extending the project’s impact beyond immediate research outcomes. The READINESS platform’s design allows researchers to simulate experiments and conduct them remotely, with artificial intelligence continuously refining testing parameters for improved results.

This award reflects the growing recognition of SUNY Poly as a national leader in semiconductor innovation, advanced manufacturing, and applied artificial intelligence.

SUNY Poly President Dr.
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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