Date: March 6, 2025
Speaker: Santiago Miret, AI Researcher, Intel Labs
Abstract: Machine Learning (ML) methods that can process large amounts of heterogenous data have tremendous potential to accelerate the end-to-end discovery, synthesis, and characterization of novel materials to address global-scale challenges like clean energy, sustainable semiconductor manufacturing and drug discovery. In this talk, I will present an overview of Intel Labs’ research efforts and community engagement efforts on ML for materials discovery along with technical deep dives focusing on two ambitious goals: 1. Accelerating scientific simulation by more than 100x using geometric deep learning and software tools to enable large-scale deployment of ML models in real-world simulations; 2. Leveraging Large Language Models (LLMs) as scientific assistants to automate scientific tasks for materials discovery.
