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arXiv 2603.20141cond-mat.mtrl-sci
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基于变换器的二维材料弹性应变工程中电子性质预测

Transformer-based prediction of two-dimensional material electronic properties under elastic strain engineering

School of Materials Science and Engineering, Beijing Institute of Technology（北京理工大学材料学院）；School of Chemistry and Chemical Engineering, Beijing Institute of Technology（北京理工大学化学与化学工程学院）

Haoran Ma, Yuchen Zheng, Leining Zhang, Xiaofei Chen, Dan Wang

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AI总结：

本文提出基于变换器的多目标替代模型框架，实现DFT级别的带隙预测精度，通过注意力权重分析保持可解释性，揭示剪切应变对带隙和声子稳定性的影响，为材料信息学加速深度弹性应变工程提供通用策略。

摘要：

应变工程为调节二维材料电子性质提供了有力途径，但利用密度泛函理论（DFT）探索全多维应变空间计算上不可行，因为正应变和剪切分量之间存在非线性耦合。本文介绍了一种基于变换器的多目标替代模型框架，实现了DFT级别的带隙预测精度，达到均方误差0.0103 eV，通过注意力权重分析保持完全可解释性。所学的自注意力图一致地识别出剪切应变作为影响带隙和声子稳定性的相互作用中心，这一见解难以通过经典特征重要性度量捕捉。本文建立了基于注意力的架构作为多性质预测的物理可解释替代模型，为材料信息学加速深度弹性应变工程提供通用策略。

英文摘要：

Strain engineering provides a powerful route for tuning the electronic properties of two-dimensional (2D) materials, but exploring the full multidimensional strain space with density functional theory (DFT) is computationally prohibitive due to the nonlinear coupling between normal and shear components. In this work, we introduce a Transformer-based, multi-target surrogate model framework that achieves DFT-level bandgap prediction accuracy, reaching a mean absolute error of 0.0103 eV while retaining full interpretability through attention-weight analysis. The learned self-attention map consistently identifies shear strain as the interaction center that influences both bandgap and phonon stability, an insight not readily captured by classical feature-importance metrics. This work establishes attention-based architectures as physically interpretable surrogate models for multi-property prediction, offering a generalizable strategy for accelerating deep elastic strain engineering in materials informatics.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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