Materials Science

Opening — Why this matters now For years, we have asked large language models to explain science. The paper behind SAGA asks a more uncomfortable question: what happens when we ask them to do science instead? Scientific discovery has always been bottlenecked not by ideas, but by coordination — between hypothesis generation, experiment design, evaluation, and iteration. SAGA reframes this entire loop as an agentic system problem. Not a chatbot. Not a single model. A laboratory of cooperating AI agents. ...

Opening — Why this matters now Large Language Models have already proven they can talk science. The harder question is whether they can do science—reliably, repeatably, and without a human standing by to fix their mistakes. Nowhere is this tension clearer than in computational materials science, where one incorrect parameter silently poisons an entire simulation chain. ...

The search for new superconductors, energy materials, and exotic compounds often begins not in a lab—but in a database. Yet despite decades of digitization, scientific knowledge remains fragmented across millions of papers, scattered ontologies, and uncharted connections. A new study from Los Alamos National Laboratory proposes an AI-driven framework that doesn’t just analyze documents—it predicts the next breakthrough. From Papers to Properties: A Three-Tiered Approach At the heart of this method is a clever ensemble pipeline that combines interpretability with predictive power. The authors start by mapping over 46,000 papers on transition-metal dichalcogenides (TMDs)—a key class of 2D materials—into a matrix of latent topics and material mentions. Then they apply a hierarchical modeling approach: ...