摘要
Recent advances in computing power have enabled the generation of large datasets for materials,enabling data-driven approaches to problem-solving in materials science,including materials discovery.Machine learning is a primary tool for manipulating such large datasets,predicting unknown material properties and uncovering relationships between structure and property.Among state-of-the-art machine learning algorithms,gradient-boosted regression trees(GBRT)are known to provide highly accurate predictions,as well as interpretable analysis based on the importance of features.Here,in a search for lead-free perovskites for use in solar cells,we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap.Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.
基金
This research was supported by the Nano·Material Technology Development Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Science and ICT(NRF-2016M3A7B4025408 and NRF-2017M3A7B4049366).
