arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.14650cond-mat.mtrl-sciphysics.comp-ph
收藏

用于预测TiO₂单层掺杂形成能的数据高效机器学习

Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO$_2$ Monolayer

University of Oulu(奥卢大学)

Kati Asikainen, Matti Alatalo, Marko Huttula, Assa Aravindh Sasikala Devi

AI总结:

本文提出利用小数据集和物理指导描述符,通过机器学习预测TiO₂单层掺杂形成能,展示了数据高效方法在材料筛选中的潜力。

摘要:

机器学习模型在材料科学中的应用日益增加,但其预测能力往往受到数据稀缺的限制。本文表明,即使在训练样本数量有限的情况下,只要数据集紧凑且基于物理相关量,也能实现准确的预测。通过结合密度泛函理论计算与机器学习框架,我们构建了基于描述符的模型,用于预测掺杂的磁铁矿TiO₂单层的形成能。首先,对单掺杂Pt配置的机器学习模型预测精度进行了评估,证明所选的结构和化学描述符能够可靠地捕捉支配掺杂稳定性的重要因素。接着,通过扩展数据集以包含Ag掺杂配置来检验化学可转移性。随着更多Ag掺杂数据点被纳入训练,预测精度系统性地提高,而Pt的性能保持稳健。这些结果突显了小而精心编纂的数据集结合物理指导的描述符,能够不仅实现准确的机器学习驱动筛选,还能实现化学上可转移的预测。

英文摘要:

Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can be achieved, even with a limited number of training examples, provided the dataset is compact and and grounded in physically relevant quantities. By combining density functional theory calculations with a machine-learning framework, we construct accurate descriptor-based models to predict the formation energies of doped lepidocrocite TiO$_2$ monolayers. The predictive accuracy of machine-learning models was first evaluated for single-dopant Pt configurations, demonstrating that the selected structural and chemical descriptors reliably capture the key factors governing dopant stability. Chemical transferability is then examined by extending the dataset to include Ag-doped configurations. Predictive accuracy improved systematically as additional Ag-doped data points were included in the training, while the performance of Pt remains robust. These results highlight the potential of small and well-curated datasets combined with physically informed descriptors to enable not only accurate but also chemically transferable machine-learning-driven screening in doped TiO$_2$ monolayer.

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。

↑