AI training method can drastically shorten time for calculations in quantum mechanics

Researchers at KAIST have developed DeepSCF, an AI-based model that speeds up complex quantum mechanical calculations by eliminating the repetitive, computationally intense self-consistent field (SCF) processes required in traditional density functional theory (DFT). Using a neural network trained on 3D chemical bonding data, the model accurately predicts quantum properties across large systems with less computational effort. This breakthrough allows faster, scalable simulations in materials science and drug design, showing promise for enhanced AI applications in quantum mechanics.

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