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Hybrid peroskite solar cells are newly emergent high-performance photovoltaicdevices, which suffer from disadvantages such as toxic elements, short-termstabilities, and so on. Searching for alternative perovskites with highphotovoltaic performances and thermally stabilities is urgent in this field. Inthis work, stimulated by the recently proposed materials-genome initiativeproject, firstly we build classical machine-learning algorithms for the modelsof formation energies, bangdaps and Deybe temperatures for hybridorganic-inorganic double perovskites, then we choose the high-precision modelsto screen a large scale of double-perovskite chemical space, to filter out goodpervoskite candidates for solar cells. We also analyze features of importancesfor the the three target properties to reveal the underlying mechanisms anddiscover the typical characteristics of high-performances double perovskites.Secondly we adopt the Crystal graph convolution neural network (CGCNN), tobuild precise model for bandgaps of perovskites for further filtering. Finallywe use the ab-initio method to verify the results predicted by the CGCNNmethod, and find that, six out of twenty randomly chosen (CH3)2NH2-based HOIDPcandidates possess finite bandgaps, and especially, (CH3)2NH2AuSbCl6 and(CH3)2NH2CsPdF6 possess the bandgaps of 0.633 eV and 0.504 eV, which areappropriate for photovoltaic applications. Our work not only provides a largescale of potential high-performance double-perovskite candidates for futuralexperimental or theoretical verification, but also showcases the effective andpowerful prediction of the combined ML and CGCNN method proposed for the firsttime here.

Discovery of Stable Hybrid Organic-inorganic Double Perovskites for  High-performance Solar Cells via Machine-learning Algorithms and Crystal  Graph Convolution Neural Network Method