# New machine learning model quickly and accurately predicts dielectric function

**URL:** https://community.materialssquare.com/t/new-machine-learning-model-quickly-and-accurately-predicts-dielectric-function/816
**Category:** News
**Created:** [October 28, 2024, 2:18pm UTC](https://community.materialssquare.com/t/new-machine-learning-model-quickly-and-accurately-predicts-dielectric-function/816 "2024-10-28T14:18:07Z")
**Posts on this page:** 1
**Page:** 1

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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 28, 2024, 2:18pm UTC](https://community.materialssquare.com/t/new-machine-learning-model-quickly-and-accurately-predicts-dielectric-function/816/1 "2024-10-28T14:18:07Z")

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Researchers at the University of Tokyo have developed a machine learning model to accurately and rapidly predict the dielectric function of materials by analyzing chemical bonds instead of individual molecules. This innovation addresses the traditionally resource-intensive calculations of dielectric function, crucial for developing materials in advanced tech fields like 6G. Validated against empirical data on simple molecules, this model performs near the accuracy of quantum mechanical methods at a fraction of the computational cost, with potential applications extending to more complex materials.

Please continue reading the full article under the link below:

> **[New machine learning model quickly and accurately predicts dielectric function](https://phys.org/news/2024-10-machine-quickly-accurately-dielectric-function.html)**
>
> Researchers Tomohito Amano and Shinji Tsuneyuki of the University of Tokyo with Tamio Yamazaki of CURIE (JSR-UTokyo Collaboration Hub) have developed a new machine learning model to predict the dielectric function of materials, rather than...

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