用机器学习预筛选半导体中的点缺陷
Prescreening Point Defects in Semiconductors With Machine Learning
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中文总结 AI 辅助
本研究探索物理引导的机器学习模型，用于预筛选半导体点缺陷，预测形成能和零声子线，在4H-SiC上实现较低误差，可加速高通量表征流程。
中文摘要 AI 辅助
利用密度泛函理论（DFT）的高通量计算通常用于探索点缺陷，以应用于电力电子和量子技术领域。目前，传统模拟技术正发生重大转变，转向机器学习（ML）方法。我们探索了一类物理引导的机器学习模型，用于预测缺陷形成能和零声子线（ZPL），以识别用于量子应用的点缺陷。这些模型专门针对加速高通量工作流程中的预筛选步骤而设计，因此旨在避免机器学习原子间势（MLIP）中通常存在的代价高昂的弛豫步骤。我们使用三种不同的描述符来表示缺陷系统，比较了岭回归、核岭回归和多层感知器（MLP）模型在4H-SiC中单点和双点缺陷系统上的性能。对于空位和替代缺陷，优化后的模型给出的形成能平均绝对误差（MAE）为0.437 eV，零声子线为0.202 eV，这刚好高于即使超出目标预筛选范围也能使此类预测有用的水平，即在某些应用中，它们可能完全取代代价高昂的DFT计算。对于间隙原子，平均绝对误差较大，形成能为1.101 eV，零声子线为0.230 eV，虽然仍可用于预筛选，但通常不适用于更详细的表征。因此，虽然通过模型设计和优化可以进一步改进结果，但本工作中提出的模型已经可用于点缺陷高通量表征中的预筛选。
英文摘要
High-throughput calculations using density-functional theory (DFT) are commonly used to explore point defects for applications in power electronics and quantum technologies. There is currently a major shift away from these traditional simulation techniques towards machine learning (ML) methods. We explore a class of physics-guided ML models for predicting defect formation energies and zero-phonon lines (ZPL) to identify point defects for quantum applications. The models are specifically targeted for use in a prescreening step for accelerated high-throughput workflows, and are therefore designed to avoid the costly relaxation step typically present with ML interatomic potentials (MLIPs). We compare performance for single and double point defect systems in 4H-SiC with ridge, kernel ridge, and multilayer perceptron (MLP) models using three different descriptors representing the defect systems. For vacancies and substitutions, the optimized models give mean absolute errors (MAEs) of 0.437 eV for the formation energy and 0.202 eV for ZPLs, which is just above the level at which such predictions can be useful even beyond the targeted prescreening, i.e., in some applications they may completely replace the need for costly DFT calculations. For interstitials the MAEs are larger, 1.101 eV for the formation energy and 0.230 eV for the ZPL, which, while still useful for prescreening, will not generally be useful for more detailed characterization. Hence, while the results may be further improved by model design and optimization, the models presented in this work are already useful for prescreening in high-throughput characterization of point defects.
发表机构
- Linköping University（林雪平大学）
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