Speakers
Description
Artificial intelligence and machine learning (AI/ML) are transforming experimental condensed matter physics and material science, enhancing the discovery rate. Our work focuses on colloidal metal-halide perovskite quantum dots, which are versatile nanomaterials with strong potential as LEDs, quantum light sources, and other optoelectronic devices. Owing to their broad compositional tunability and simple fabrication, these materials have attracted significant academic attention, with over 1,500 publications per year in 2024. In this scientific landscape, AI/ML promises to make a significant impact in three key areas: managing the growing volume of primary experimental data relative to derivative results, enhancing reproducibility across research groups, and ultimately, optimizing complex synthesis parameters to accelerate material discovery. Recent advances have shown that large language models (LLMs) may be used for encoding, translation, and optimization of experimental procedures, as well as prediction of experimental outcomes. In this contribution, we propose an LLM-driven agentic framework that facilitates an ontology-supported encoding of fabrication procedures and material properties, learns continuously from the literature, streamlines research workflows for perovskites, and enhances reproducibility to accelerate discovery.
| Broad physics domain | condensed matter physics |
|---|---|
| AI/ML technique(s) to be presented | natural language processing, semantic network, LLM |
