Bandgap model using symbolic regression for environmentally compatible lead-free inorganic double perovskites
2022 IEEE 49th Photovoltaics Specialists Conference (PVSC)(2022)
摘要
Data-driven models have become an essential practice of scientific research in the perovskite field, along with theory and experiments. Material informatics has emerged as a viable alternative method of exploring and formulating novel perovskite compounds using a descriptor-based approach. Herein, we develop a method that includes feature augmentation with symbolic regression to rapidly estimate and screen non-toxic lead-free inorganic double perovskites (A
2
BB'X
6
) using machine learning. Predictive models were created by identifying a physico-chemical relevant descriptor from an extensive pool of augmented features. Using primary atomic and molecular features, a high dimensional space of descriptors
$(\text{containing}\approx 3\times 10^{5}$
features) was reconstructed using mathematical operators. By increasing the complexity from 1-D to 5-D descriptor, the correlation coefficient was increased from 81.6% to 92.4%. These accurate and interpretable models can then be employed for screening lead-free perovskites with appropriate bandgaps and stability.
更多查看译文
关键词
symbolic regression,lead-free
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要