A research team led by Anima Anandkumar at the California Institute of Technology published a paper on Aug. 24 describing an AI-assisted approach to density functional theory, or DFT, that shifts a key computational step from cubic scaling to near-linear behavior. The work targets one of the longest-running bottlenecks in quantum chemistry, where simulation costs rise sharply as molecular or material systems grow. Instead of asking a model to predict the final answer in one shot, the team trained a Fourier Neural Operator, or FNO, to learn the forward map from input conditions to electron density, then inserted that model back into the standard self-consistent DFT loop. The paper reports complexity dropping from O(N^3) to O(N log N) for that step. In tests on drug-scale molecules outside the training set, the Kohn-Sham FNO posted a density error of 2.23%, versus 9.97% for a direct-prediction baseline; at 45 heavy atoms, the gap widened to 4% versus 41%. For large-scale validation, the researchers used magnesium dislocation structures. The paper says the new method converged on a system with 8,250 atoms and 82,500 valence electrons using a single NVIDIA B300 GPU. It contrasts that with a 2019 full DFT calculation on 6,164 magnesium atoms that used about 7,800 NVIDIA V100 GPUs on the Summit supercomputer.