Combining Experiments, Large Language Models, and Theory to Discover Quantum Materials

The discovery of quantum materials, from unconventional superconductors to topological and correlated systems, has traditionally relied on a slow and fragmented loop between theory, computation, and experiment. Serendipity and brute force trial-and-error have played an important role in many important discoveries. In this talk, I will describe how Periodic Labs is rethinking this loop by integrating experiments, large language models (LLMs), and physical theory into a unified discovery engine. Our approach aims to accelerate experimental iteration by automating characterization and analysis. I will discuss how LLMs can be used as reasoning interfaces over physical constraints, experimental data, and scientific literature. 

Speaker: Ekin Dogus Cubuk, Google DeepMind