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arXiv 2111.07557cond-mat.mtrl-sci
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基于凝聚层次聚类的双钙钛矿光伏材料筛选

Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering

Utkarsh Saha, Koyendrila Debnath, Soumitra Satapathi

摘要:

数据驱动的方法来解决材料科学中的问题,由于其能够预测未知的材料特性并揭示结构和特性之间的关系,近年来已经获得了极大的普及。机器学习算法,如GBRT,随机森林和神经网络,在预测材料的目标特性和各种应用的结构设计方面取得了巨大的成功。然而,使用这些算法在所需精度内实现结果的主要缺点是需要大型数据集,当数据不足以用于训练模型时,这对于问题来说可能是具有挑战性的。在这项工作中,我们提出了一个层次聚类算法,它可以更好地工作在小数据集约束的材料科学问题的使用。我们应用该算法筛选出有前途的双钙钛矿材料作为太阳能电池的候选者。

英文摘要:

Data-driven approaches to solve problems in materials science have gained immense popularity in recent times due to their ability to predict unknown material properties and uncover relationships between structure and property. Machine learning algorithms like GBRT, random forest and neural networks have had tremendous success in predicting target properties of materials and design of structures for various applications. However, a major drawback for achieving results within the required accuracy using these algorithms has been the need for large datasets which can be challenging for problems when data is not sufficiently available for training the models. In this work, we propose the use of a hierarchical clustering algorithm which can work considerably better on materials science problems with small dataset constraints. We apply the algorithm to screen out promising double perovskite materials as candidates for solar cells.

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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