Researchers have developed FAIRChem v2, a machine-learning framework that enables unified atomistic simulation across various domains, including molecular chemistry, catalysis, and inorganic materials. The framework uses a universal machine-learning interatomic potential, allowing for simulations to be conducted across different areas. This development is significant for advancing our understanding of complex molecular interactions and properties. It matters because it has the potential to accelerate scientific discovery and innovation in fields such as materials science and chemistry.