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arXiv 2406.08191cond-mat.mtrl-sci
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光谱的深度学习:预测半导体的介电函数

Deep learning of spectra: Predicting the dielectric function of semiconductors

Malte Grunert, Max Großmann, Erich Runge

AI总结:

研究构建了含9915个介电张量的从头算数据库,并提出基于图注意力神经网络的OptiMate模型,仅凭晶体结构即可准确预测半导体的介电函数和折射率,生成无伪影的平滑光谱曲线。

摘要:

基于机器学习预测晶体材料的光谱及其相关性质(如介电函数)具有巨大的、迄今为止尚未探索的技术潜力。为此,我们创建了一个从头算数据库,包含在独立粒子近似(IPA)下计算的9915个半导体和绝缘体的介电张量。此外,我们提出了OptiMate机器学习模型家族,这是一系列图注意力神经网络(GAT),经过训练用于预测介电函数和折射率。OptiMate仅使用半导体的晶体结构即可实现对其光谱的准确预测。在无需通过惩罚项强制约束这些性质的情况下,获得了平滑且无伪影的曲线。

英文摘要:

Predicting spectra and related properties such as the dielectric function of crystalline materials based on machine learning has a huge, hitherto unexplored, technological potential. For this reason, we create an ab initio database of 9915 dielectric tensors of semiconductors and insulators calculated in the independent-particle approximation (IPA). In addition, we present the OptiMate family of machine learning models, a series of graph attention neural networks (GAT) trained to predict the dielectric function and refractive index. OptiMate yields accurate prediction of spectra of semiconductors using only their crystal structure. Smooth, artifact-free curves are obtained without these properties being enforced by penalties.

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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