论文标题
全球财产预测：一项关于开源的基准研究，类似钙钛矿的数据集
Global property prediction: A benchmark study on open source, perovskite-like datasets
论文作者
论文摘要
新型材料的筛选组合空间（例如钙钛矿样的光伏材料）导致了大量模拟的高量值数据及其分析。这项研究提出了基于结构指纹的机器学习模型的全面比较，该模型在七个类似钙钛矿的材料的开源数据库上，以预测带镜和能量。它表明，给定的方法均无法均匀地捕获任意数据库，同时强调通常使用的指标是典型的工作流程中的高度数据库。此外，选择方差选择和自动编码器可显着降低指纹尺寸的适用性，这表明使用常见指纹构建的模型仅依赖于可用指纹空间的子手机。
Screening combinatorial space for novel materials - such as perovskite-like ones for photovoltaics - has resulted in a high amount of simulated high-troughput data and analysis thereof. This study proposes a comprehensive comparison of structural-fingerprint based machine-learning models on seven open-source databases of perovskite-like materials to predict bandgaps and energies. It shows that none of the given methods are able to capture arbitrary databases evenly, while underlining that commonly used metrics are highly database dependent in typical workflows. In addition the applicability of variance selection and autoencoders to significantly reduce fingerprint size indicates that models built with common fingerprints only rely on a submanifold of the available fingerprint space.
