Artificial Intelligence Materials Institute
Artificial Intelligence Materials Institute

News & Events

News, updates, and upcoming events from AI-MI highlighting research advances, partnerships, and people accelerating AI-driven materials discovery for energy, sustainability, and quantum technologies.

Upcoming NVIDIA GTC Session with Eun-Ah Kim

Mar 2, 2026 | ,

Visionary thought leaders Eun-Ah Kim (Cornell University) and Marinka Zitnik (Harvard Medical School) join AI conference NVIDIA GTC to share how their research teams are applying robust, domain-specific AI frameworks to reshape the future of their fields.

Now Recruiting Institute Postdocs!

Jan 5, 2026 | , ,

AI-MI seeks postdoctoral researchers to drive rapid, AI-enabled discovery of next-generation materials for energy, sustainability and quantum technologies. Join our interdisciplinary team advancing reproducible, data-centric materials science.

Brown Joins Cornell to Bring Expertise to AI Materials Institute

Sep 15, 2025 |

Professor Keith Brown (ME, MSE, Physics) will lead BU’s involvement in the institute. Brown is perhaps best known for his Bayesian experimental autonomous researcher (BEAR) system, a self-driving lab that combines machine learning, robotics, additive manufacturing, and mechanical testing.

Upcoming Events

AI-MI Seminar Series: Literature-Informed Agents for Drug Discovery

Seminar

Discover how researchers are using AI to extract chemical and biological data from scientific literature and patents. These large-scale datasets are enabling faster molecule design and advancing AI-driven therapeutic discovery.

AI-MI Seminar Series: From Entropy to Epiplexity – Rethinking Information for Computationally Bounded Intelligence

Seminar

What can a computationally bounded learner actually extract from data — and can that amount exceed what the generating process itself contained? In this AI-MI Seminar, NYU Professor Andrew Gordon Wilson introduces epiplexity, a new formalization of information that targets structured, learnable content and offers a principled foundation for data selection in modern AI systems.

AI-MI Seminar Series: Learning, Understanding, and Predicting Quantum Phases in Two-Dimensional Materials

Seminar

The many-electron problem has resisted solution for decades — but neural networks are changing that. In this AI-MI Seminar, Flatiron Institute's Shiwei Zhang presents a physics-inspired computational approach that has already surpassed state-of-the-art methods for two-dimensional electron systems, revealing exotic quantum phases and opening new frontiers in materials science.

AI-MI Annual Meeting

Members of the NSF AI Materials Institute will convene for the AI-MI Annual Meeting, a two-day, in-person meeting of the NSF AI Materials Institute community.
Past Notable Events
Events Archive