%0 Journal Article %1 lu2018accelerated %A Lu, Shuaihua %A Zhou, Qionghua %A Ouyang, Yixin %A Guo, Yilv %A Li, Qiang %A Wang, Jinlan %D 2018 %J Nature Communications %K additionally machine_learning new_material perovskite theoretical %N 1 %P 3405-- %R 10.1038/s41467-018-05761-w %T Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning %U https://doi.org/10.1038/s41467-018-05761-w %V 9 %X Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.

@article{lu2018accelerated, abstract = {Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.}, added-at = {2018-09-05T14:18:13.000+0200}, author = {Lu, Shuaihua and Zhou, Qionghua and Ouyang, Yixin and Guo, Yilv and Li, Qiang and Wang, Jinlan}, biburl = {https://www.bibsonomy.org/bibtex/23f2ede43259bf41e4912a455bfad642b/bretschneider_m}, doi = {10.1038/s41467-018-05761-w}, interhash = {a588072ddf719c8df33d9086c7450878}, intrahash = {3f2ede43259bf41e4912a455bfad642b}, issn = {20411723}, journal = {Nature Communications}, keywords = {additionally machine_learning new_material perovskite theoretical}, number = 1, pages = {3405--}, refid = {Lu2018}, timestamp = {2018-09-05T14:18:13.000+0200}, title = {Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning}, url = {https://doi.org/10.1038/s41467-018-05761-w}, volume = 9, year = 2018 }