seminar series
This bi-weekly series brings together leading researchers at the intersection of artificial intelligence, machine learning, and materials science. It highlights advances in AI-enabled discovery and data-driven materials research while fostering interdisciplinary collaboration and innovation.
Join us live or watch the recordings at youtube.com/@AIMaterialsInstitute
Upcoming Seminars
To be announced...
Past Seminars
Analyzing the Nonlocality of Sparse Autoencoder Features
Streamed on May 7, 2026
This talk examines how tools from machine learning and theoretical physics can be combined to better understand the internal representations of LLMs. Drawing inspiration from holographic duality, the speaker introduces a novel entropy-based measure to quantify how nonlocal learned features are in relation to input tokens—offering new insight into how information is structured and processed in these systems.
Learning, Understanding, and Predicting Quantum Phases in Two-Dimensional Materials
Streamed live on Apr 23, 2026
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.
From Entropy to Epiplexity – Rethinking Information for Computationally Bounded Intelligence
Streamed live on Apr 9, 2026
This talk challenges foundational assumptions in information theory — asking whether computationally bounded observers can extract more from data than classical frameworks like Shannon entropy or Kolmogorov complexity suggest. The speaker introduces epiplexity, a new formalization of information that captures what real-world learners can actually use, with practical implications for how we select, generate, and transform data to build better AI systems.
Literature-Informed Agents for Drug Discovery
Streamed live on Mar 26, 2026
Join us to explore how researchers are using AI to unlock chemical and biological knowledge hidden in scientific literature. The talk will highlight new approaches for automatically extracting data from papers and patents to build large-scale datasets that power molecule and protein design—advancing drug discovery, molecular property prediction, and AI-driven therapeutic development.
Recovering Molecular Heterogeneity using Molecular Simulation, Electron Microscopy, and ML
Streamed live on Mar 12, 2026
Join us in exploring how researchers are combining molecular simulation, machine learning, and electron microscopy to better understand structural heterogeneity in complex chemical systems. This talk is presented by Erik Thiede, Assistant Professor of Chemistry at Cornell University.
Polymer Biomaterials in a Self-Driving Lab
An Artificial Intelligence Era of Magnetism
Recovering Molecular Heterogeneity using Molecular Simulation, Electron Microscopy, and ML
Streamed live on Mar 12, 2026
Join us in exploring how researchers are combining molecular simulation, machine learning, and electron microscopy to better understand structural heterogeneity in complex chemical systems. This talk is presented by Erik Thiede, Assistant Professor of Chemistry at Cornell University.