From Specialist to Generalist Models: Boosting Microscopy Analysis in Materials AI

The shift from specialist to generalist models marks a pivotal transformation in materials AI, redefining how we tackle complex challenges in microscopy analysis. Traditionally, specialist models are designed for narrowly focused tasks within specific domains, leveraging domain-specific knowledge and curated datasets to achieve high accuracy. While effective within these confines, such models are often limited in flexibility and scalability. In contrast, the emergence of foundation models - such as transformers - has unlocked new possibilities for generalist models that can address diverse tasks without extensive customization. This presentation delves into this evolution with a focus on electron microscopy. We first illustrate the strengths of specialist models in uncovering "invisible" information within microscopy images. For instance, we demonstrate how in-situ TEM, combined with specialist models, can automatically track dislocation dynamics, capturing the "jerky" motion of dislocations in high-entropy alloys, enabling detailed avalanche statistics. Transitioning towards generalist approaches, we then explore self-supervised and unsupervised analysis, e.g., on HR-TEM datasets of nanoparticles, highlighting the potential for broad adaptability. Finally, we introduce the transformative promise of generalist foundation models in materials science. These models pave the way for breakthroughs in microscopy and beyond, offering unprecedented flexibility, scalability, and insights for next-generation materials discovery