# Aligning heterogeneous DFT datasets: a graph neural network approach to cross-functional formation energies

Source: [Physics : nature.com subject feeds](https://www.nature.com/articles/s41524-026-02366-6) · 2026-11-10 · Physics
Page: https://themorning.science/story/aligning-heterogeneous-dft-datasets-a-graph-neural-network-approach-to-cross-fun-45c2b97748baca4d
Kind: research · Impact: low

## In brief

- A graph neural network approach is proposed to align heterogeneous DFT datasets.
- The method targets cross-functional formation energies, meaning energies computed with different DFT functionals.
- The article is listed in the Nature subject feed for Physics dated 2026-11-10.

## Why it matters

Formation energies computed with different DFT functionals are hard to compare directly, so a method that aligns these datasets could make materials data from different sources easier to combine.

## Timeline

- 2026-11-10: Article listed in Nature Physics subject feed

## Coverage

- [Aligning heterogeneous DFT datasets: a graph neural network approach to cross-functional formation energies](https://www.nature.com/articles/s41524-026-02366-6) — Physics : nature.com subject feeds, 2026-11-10

Tags: density functional theory, graph neural networks, formation energy, materials informatics, dataset alignment

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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