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Importance of structural deformation features in the prediction of hybrid perovskite bandgaps
Computational Materials Science ( IF 3.3 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.commatsci.2020.109858
Heesoo Park , Raghvendra Mall , Adnan Ali , Stefano Sanvito , Halima Bensmail , Fedwa El-Mellouhi

Abstract Given the surging growth of artificial-intelligence-inspired computational methods in materials science, experimental laboratories around the globe have become open to adopting data-driven approaches for materials discovery. The field witnesses emerging machine-learning models trained over databases, of which data are collected from high-throughput experimentation or first-principles calculation. Here, we address the impediment of constructing a highly accurate predictor for perovskite bandgap when the inorganic network undergoes the deformation. The predictor is trained on a dataset of first-principles calculations of pure and mixed-cation hybrid perovskites. We investigate the impact of the inclusion/exclusion of structural deformation features by training the model carefully. A high level of accuracy could be achieved with a scrupulous investigation of the input features. Our analysis emphasizes how important the feature selection is for the construction of the predictive model as we challenge the robustness of our machine learning predictor in a lab validation setup.

中文翻译:

结构变形特征在混合钙钛矿带隙预测中的重要性

摘要 鉴于材料科学中受人工智能启发的计算方法的迅猛发展,全球的实验实验室已经开始采用数据驱动的方法进行材料发现。该领域见证了在数据库上训练的新兴机器学习模型,其中数据是从高通量实验或第一性原理计算中收集的。在这里,我们解决了当无机网络发生变形时为钙钛矿带隙构建高精度预测器的障碍。预测器在纯和混合阳离子混合钙钛矿的第一性原理计算数据集上进行训练。我们通过仔细训练模型来研究包含/排除结构变形特征的影响。通过对输入特征的仔细调查,可以实现高水平的准确性。我们的分析强调了特征选择对于构建预测模型的重要性,因为我们在实验室验证设置中挑战了机器学习预测器的稳健性。
更新日期:2020-11-01
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