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

**URL:** <https://community.materialssquare.com/t/ai-training-method-can-drastically-shorten-time-for-calculations-in-quantum-mechanics/831>\
**Category:** News\
**Created:** [October 31, 2024, 4:26pm UTC](https://community.materialssquare.com/t/ai-training-method-can-drastically-shorten-time-for-calculations-in-quantum-mechanics/831 "2024-10-31T16:26:41Z")\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [October 31, 2024, 4:26pm UTC](https://community.materialssquare.com/t/ai-training-method-can-drastically-shorten-time-for-calculations-in-quantum-mechanics/831/1 "2024-10-31T16:26:41Z")

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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.

For more details, please continue reading the full article under the following link:

> **[AI training method can drastically shorten time for calculations in quantum...](https://phys.org/news/2024-10-ai-method-drastically-shorten-quantum.html)**
>
> The close relationship between AI and highly complicated scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for devising AI for their respective fields of study. KAIST...

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