# Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach

arXiv: https://arxiv.org/abs/2411.16483 · HTML: https://mlsanity.com/paper/2411.16483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds](https://mlsanity.com/paper/1802.08219/) (2018)
- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [Connectivity Optimized Nested Graph Networks for Crystal Structures](https://mlsanity.com/paper/2302.14102/) (2023)
- [Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids](https://mlsanity.com/paper/2210.16190/) (2022)
- [Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction](https://mlsanity.com/paper/2302.01486/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beyond DFT electronic band structures via dual-space orbital learning](https://mlsanity.com/paper/2610.19919/) (2026)

## Abstract (arXiv)

Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are accurate but computationally intensive. Recent years, machine learning (ML) has been extensively applied to this field, while existing ML models predominantly focus on band gap predictions or indirect band structure estimation via solving predicted Hamiltonians. An end-to-end model to predict band structure accurately and efficiently is still lacking. Here, we introduce a graph Transformer-based end-to-end approach that directly predicts band structures from crystal structures with high accuracy. Our method leverages the continuity of the k-path and treat continuous bands as a sequence. We demonstrate that our model not only provides accurate band structure predictions but also can derive other properties (such as band gap, band center, and band dispersion) with high accuracy. We verify the model performance on large and diverse datasets.