Industry, academic, and national-laboratory leaders gathered in Corning, New York, to examine AI’s potential to advance materials discovery, process innovation, and smart manufacturing.
CORNING, N.Y. — Designers, futurists, scientists, and technologists gathered at Corning Headquarters on October 1–2 for the 2026 Corning Materials Innovation Summit. Attendees from industry, academia, and national laboratories explored this year’s theme: “AI for Materials Research, Design & Manufacturing.”
The two-day event featured technical presentations, panel discussions, audience Q&A, and networking. The Summit marked a meaningful return to an in-person format after virtual editions in 2021 and 2023, enabling direct dialogue and new connections across the broader materials community.
“Being together in person creates space for the conversations that often lead to new ideas and lasting connections,” said Steven Ogunwumi, director, Academia & Government Programs at Corning. “The Summit brought together different perspectives across science, technology, and manufacturing, helping us identify opportunities to learn from one another and move innovation forward.”
The 2026 gathering was the sixth installment of a series launched in 2014 as the Corning Glass Summit. Its evolution into the Corning Materials Innovation Summit reflects an expanded technical scope—one rooted in Corning’s glass-science heritage while also encompassing ceramics, glass-ceramics, and other materials, as well as the processes that bring them to scale. Each Summit convenes the broader community around timely questions and emerging opportunities in materials innovation.
“AI is creating new possibilities across materials research, design, and manufacturing,” said Jaymin Amin, senior vice president and Chief Technology Officer. “To realize that potential, we need to combine new capabilities with deep scientific knowledge and build partnerships that help translate promising ideas into meaningful innovation faster.”
Accelerating materials discovery
Day 1, AI-Driven Materials Discovery & Design, examined how AI and machine learning can help researchers identify, develop, and understand new materials. Across two sessions, speakers considered scalable and physics-informed models, autonomous laboratories, real-time characterization, quantum simulation, large language models, and agentic workflows that can support scientific research.
A central theme was the potential to link models, simulations, robotics, and high-throughput experiments in closed loops. By learning from each experiment and using the resulting data to guide the next, researchers may be able to screen promising candidates more efficiently, test hypotheses faster, and explore complex materials-design spaces in new ways.
The presentations also reinforced that AI must remain grounded in science. Researchers and engineers are essential to asking the right questions, interpreting findings, investigating unexpected results, and determining whether a model’s output holds up in the real world.
“We see AI as a tool to amplify the work of scientists and engineers—not to replace the expertise, judgment, and curiosity they bring to the lab,” Jaymin said.
Discussions also addressed the responsibilities that accompany these emerging capabilities. AI tools become more valuable when they can access relevant, high-quality data, but that access must be balanced with rigorous data governance and protection of intellectual property and proprietary information. Model robustness, experimental validation, and safeguards for autonomous systems were recurring considerations throughout the day.
Connecting discovery and manufacturing
Day 2, AI-Enabled Process Innovation & Smart Manufacturing, shifted the focus to translating materials advances into high-quality products that can be manufactured at scale. Sessions addressed AI-ready data pipelines, process control, digital twins, supply-chain resilience, machine-augmented manufacturing, and the path from prediction to increasingly autonomous systems.
The day’s presentations emphasized that materials development and manufacturing are closely connected. Integrating materials data with process information can help teams anticipate manufacturability earlier, optimize process conditions, detect anomalies, and reduce time-consuming development cycles.
Peter Ronco, vice president, Corporate Product & Process Development, connected this opportunity to the company’s broader approach to innovation.
“What is most exciting about AI is its potential to help turn invention into innovation,” Peter said. “By bringing materials knowledge and process understanding together earlier, we can shorten the path from a promising idea to a solution that addresses a meaningful need.”
The conference was organized into four sessions over two days. Each session featured four invited speakers and concluded with a moderated panel discussion, giving attendees an opportunity to engage directly with the speakers, ask questions, and explore the day’s topics in greater depth.
These exchanges raised questions of shared importance to academia and industry: How can educational institutions and workplaces equip students and early-career professionals to use AI effectively? How can organizations preserve hard-won technical knowledge? And how can the next generation apply powerful new tools with human discernment, deep domain expertise, and sound judgment?
A forum for responsible progress
The 2026 Summit reflected a long-term perspective on the role AI may play in research and innovation. Rather than treating AI as a standalone solution, speakers and attendees considered how models, data, experiments, and human judgment can work together to support stronger decisions and more productive research and manufacturing systems.
In his Day 2 opening remarks, Claudio Mazzali, vice president, Global Research, highlighted complementary priorities for advancing the field: scaling AI with scientific grounding, closing the experimental loop through autonomous experimentation and characterization, expanding the discovery toolkit, and building capable AI research partners.
“This year’s Summit underscored that the greatest opportunity is not AI on its own, but AI integrated with scientific understanding, experiments, and quality data,” Claudio said. “Those connections can help Corning strengthen how we discover, develop, and scale new technologies while keeping human expertise at the center of the work.”
As the AI landscape continues to evolve, this year’s Summit created a forum for examining both its possibilities and practical challenges. The return to face-to-face exchange reinforced the value of sharing expertise, building relationships, and developing responsible approaches to the next chapter of materials innovation—as the field advances to take on new, more complex scientific and technological questions.
