用图Transformer直接从晶体结构预测能带，精度高且端到端。
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
- 将能带路径视为连续序列，用图Transformer建模
- 能带、带隙、带中心等性质预测准确率高
- 适合材料设计与性质预测的科研人员
从晶体结构预测电子能带结构对理解材料的构效关系至关重要。第一性原理方法虽精确但计算成本高。近年来机器学习被广泛应用于该领域，但现有模型多集中于带隙预测或通过求解预测哈密顿量间接估算能带结构。尚缺乏一种能高效、准确端到端预测能带结构的方法。本文提出一种基于图Transformer的端到端方法，可直接从晶体结构预测能带结构，具有高精度。该方法利用k路径的连续性，将连续能带视为序列进行建模。实验表明，该模型不仅在能带预测上表现优异，还能高精度推导出带隙、带中心和能带色散等其他关键性质。模型在大规模多样化数据集上进行了验证。
原文摘要 · Abstract (English)
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.
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