# Adaptive structural fusion for dielectric constant prediction using physics-guided graph neural networks

Source: [Physics : nature.com subject feeds](https://www.nature.com/articles/s41598-026-78263-1) · 2026-11-12 · Physics
Page: https://themorning.science/story/adaptive-structural-fusion-for-dielectric-constant-prediction-using-physics-guid-9121816b1e49a68f
Kind: research · Impact: low

## In brief

- A paper titled "Adaptive structural fusion for dielectric constant prediction using physics-guided graph neural networks" was listed in Nature's Physics subject feed on 2026-11-12.
- The work uses graph neural networks guided by physics to predict dielectric constants.
- No summary was available in the source, so details of methods and results are unconfirmed.

## Why it matters

It describes a machine-learning approach for predicting a material property that matters for electronics and materials design, which may interest researchers using computational screening.

## Coverage

- [Adaptive structural fusion for dielectric constant prediction using physics-guided graph neural networks](https://www.nature.com/articles/s41598-026-78263-1) — Physics : nature.com subject feeds, 2026-11-12

Tags: graph neural networks, dielectric constant, materials informatics, physics-guided machine learning

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Brief generated by AI (claude-haiku-5-5) from the reporting listed under Coverage. Headlines belong to their publishers. The Morning Science – https://themorning.science