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. ...
Opening — Why this matters now Scientific software has a strange tradition: world‑class physics wrapped in workflows that feel frozen in the 1990s. Seismology is no exception. SPECFEM — arguably the gold standard for seismic wave simulation — delivers extraordinary numerical fidelity, but only after users survive a rite of passage involving fragile text files, shell scripts, and MPI incantations. ...
Opening — Why this matters now Scientific discovery has always been bottlenecked by one thing: human bandwidth. In scientific machine learning (SciML), where physics meets data-driven modeling, that bottleneck shows up as painstaking trial and error—architectures tuned by hand, loss functions adjusted by intuition, and results validated by weeks of computation. Enter AgenticSciML, a new framework from Brown University that asks a radical question: What if AI could not only run the experiment, but design the method itself? ...