# AI and DFT Computationally Design Catalysts to Destroy "Forever Chemicals" Before Synthesis

**URL:** <https://community.materialssquare.com/t/ai-and-dft-computationally-design-catalysts-to-destroy-forever-chemicals-before-synthesis/1281>\
**Category:** News\
**Created:** [May 15, 2026, 3:06am UTC](https://community.materialssquare.com/t/ai-and-dft-computationally-design-catalysts-to-destroy-forever-chemicals-before-synthesis/1281 "2026-05-15T03:06:15Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Ahn\_Yurim](https://avatars.discourse-cdn.com/v4/letter/a/c5a1d2/32.png) [@Ahn\_Yurim](https://community.materialssquare.com/u/Ahn_Yurim)\
**Post date:** [May 15, 2026, 3:06am UTC](https://community.materialssquare.com/t/ai-and-dft-computationally-design-catalysts-to-destroy-forever-chemicals-before-synthesis/1281/1 "2026-05-15T03:06:15Z")

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Researchers combined AI with Density Functional Theory (DFT) to perform inverse design of transition metal-doped spinel ferrite catalysts capable of breaking the strong C-F bonds in PFAS (forever chemicals).

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

> **[Predicting the Unbreakable: How AI and Density Functional Theory (DFT) are...](https://communities.springernature.com/posts/predicting-the-unbreakable-how-ai-and-density-functional-theory-dft-are-designing-pfas-destroying-catalysts)**
>
> For decades, catalyst discovery has relied on slow, resource-heavy empirical chemistry. This trial-and-error paradigm cannot scale to meet the PFAS crisis. By merging machine learning with Density Functional Theory (DFT), we can now computationally...

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