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      Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

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          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.

          Abstract

          Conventional trial-error method is inefficient in discovering new functional materials in vast chemical and structural space. Here Lu et al. use machine learning techniques to screen out the most promising lead-free organic-inorganic perovskites with proper bandgap and stability from thousands of them in a flash.

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          Generalized Gradient Approximation Made Simple

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            Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

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              Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

              Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.
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                Author and article information

                Contributors
                jlwang@seu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 August 2018
                24 August 2018
                2018
                : 9
                : 3405
                Affiliations
                ISNI 0000 0004 1761 0489, GRID grid.263826.b, School of Physics, , Southeast University, ; Nanjing, 211189 China
                Article
                5761
                10.1038/s41467-018-05761-w
                6109147
                30143621
                043bd141-bd93-4441-9aae-fbccf8342b98
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 April 2018
                : 7 July 2018
                Funding
                Funded by: 1. National Key Research and Development Program of China (2017YFA0204800) 2. Natural Science Funds of China (21525311, 21773027) 3. Jiangsu 333 project (BRA2016353) 4. Fundamental Research Funds for the Central Universities of China
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