Visual Tech Advancements in Materials Science Structuring New Material Discovery & Development

Historically, materials discovery has relied on slow, costly trial-and-error methods. Artificial intelligence and machine learning are now transforming this process by enabling faster, more creative solutions to long-standing challenges. Generative AI models are expanding beyond composition to include processing, form and real-world constraints – accelerating the creation of new molecules, chemicals and materials across industries.

At our 11th Annual LDV Vision Summit earlier this year, a panel of distinguished experts explored the future of materials science powered by visual tech and AI:

Dr. David Smith is the Associate Chair and James B. Duke Distinguished Professor of ECE at Duke University, where he also directs the Center for Metamaterials and Integrated Plasmonics. He holds adjunct positions at UC San Diego and Imperial College London.

Dr. Kristin Schmidt, Strategy Assistant for Accelerated Discovery & Vice-Chair of Physical Sciences Council at IBM, leads the Accelerated Materials Discovery group at IBM Research Almaden.

Dr. Chirranjeevi Gopal, Co-Founder & CTO at Mitra Chem, the first lithium-ion battery materials manufacturer focused on shortening the lab-to-production timeline by over 90%, addressing the largest barrier to innovation: R&D and scale-up speed.

Dr. Amanda Petford-Long, Director of the Materials Science Division at Argonne National Lab. As Argonne Distinguished Fellow in the Materials Science Division, she participates in a BES-funded research program, and is currently leading the Argonne Microelectronics Institute.

Moderator: Ash Cleary, Associate at LDV Capital.

Visual Tech Advancements in Materials Science Structuring New Material Discovery & Development - YouTube

Check out the recording or read our lightly edited transcript below.

Amanda: I’ve been at Argonne National Lab for nearly 19 years. Visual tech is something we're using all the time. We rely heavily on visual data and we need AI to process it, understand it and then do something useful with it.

Chirranjeevi: Our key differentiator at Mitra Chem is using machine learning and other tools to accelerate the process from developing a material in the lab to scaling it for manufacturing. By training, I’m a chemist, but most of my career has been at the intersection of data science and building physical devices.

David: My area of research is metamaterials – artificially structured materials – so I’m a bit adjacent to traditional materials science. I focus on the confluence of advanced materials and artificial structures. Our work led to the development of metamaterials when we created a material with a negative index of refraction back at UCSD. It became a poster child for metamaterials – a material that can’t exist in nature, first predicted by a Russian physicist in the 1960s. That breakthrough, followed by the invisibility cloak we developed at Duke, helped spark the field of metamaterials. Around 2012, I began focusing on practical applications and spinning off companies related to metamaterials. I’m particularly interested in how we replicate material functionality with artificial structures.

Kristin: I'm also a chemist by training but have ventured into the world of AI. My group develops AI for scientific applications, specifically identifying concerning materials and replacing them with safer alternatives. We also focus on building AI models that collaborate with human experts – because none of us can do it alone.

Ash: What present-day trends and opportunities in leveraging visual tech for new material discovery and development are exciting you the most?

Chirranjeevi: I can provide a unique perspective because we are both making physical materials and, as entrepreneurs who've taken investor money to build a revenue-generating business, we often have to balance taking as little risk as possible to reach revenue. That typically means relying on tried-and-tested, human intuition–guided synthesis approaches – while also doing something differentiated, like using AI-powered methods to speed up development cycles. Compared to five years ago, when techniques like Bayesian optimization were among the few ways to analyze data and accelerate development, I’m now especially excited about incorporating generative AI advances to sift through existing literature. This can potentially simplify years of work by graduate students or scientists, helping to translate what's already been done into a tangible product.

The key is leveraging AI to augment human intuition – not just to discover new materials but to synthesize and make them real, whether at lab scale or pilot scale.

Amanda: At Argonne National Lab, we work on everything from basic science to applied research and industry collaboration. I resonated with something Akhila said earlier: the best path forward is combining humans and machine learning. We still need scientists to come up with the big questions and define the problems we want to solve. Experimental validation is also absolutely essential, as she mentioned.

We often start by using AI to down-select potential materials. Then, for the materials we want to use – for example, in a microelectronics component – we’ll model and simulate them. After that, we take those materials to tools like our synchrotron or an electron microscope to image them and understand their behavior through in situ experiments. We feed that data back into our models and refine them in a circular, iterative process. That’s proving to be the best approach. But you can’t do it without scientists – at least not yet – and I hope that remains the case for a while, or we’ll all be out of jobs.