Tackling a 60-year-old challenge in quantum chemistry: making density functional theory scale nearly linearly with system size. This has huge implications for opening the door to realistic systems that have traditionally been too expensive to simulate. AI has attempted to accelerate these calculations, but models generally struggle to extrapolate, particularly to systems larger than those seen during training. Unlike text or images, quantum-mechanical training data is extremely expensive to generate. We present a single unified AI model that performs quantum-mechanical simulations of both molecules and materials in quasi-linear time. Using a novel Fourier neural operator variant, we learn the underlying Kohn–Sham equation map to produce physics-informed, self-consistent answers. Unlike prior AI approaches that directly predict chemical properties, our model works through intermediate steps to improve difficult predictions, resembling inference-time reasoning in large language models. To demonstrate its scalability, we run a self-consistent calculation of a magnesium dislocation with about 80k electrons on a single GPU, which previously required about 7k GPUs. tensorlab.cms.caltech.edu/us… @DanishK42 @Caltech