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Kouroudis I, Tanko KT, Karimipour M, Ali AB, Kumar DK, Sudhakar V, Gupta RK, Visoly-Fisher I, Lira-Cantu M, Gagliardi A. Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells. ACS ENERGY LETTERS 2024; 9:1581-1586. [PMID: 38633992 PMCID: PMC11019640 DOI: 10.1021/acsenergylett.4c00328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/09/2024] [Accepted: 03/12/2024] [Indexed: 04/19/2024]
Abstract
The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.
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Affiliation(s)
- Ioannis Kouroudis
- Department
of Electrical Engineering, School of Computation, Information and
Technology, Technical University of Munich, Hans-Piloty Strasse 1, 85748 Garching bei Munich,Germany
| | - Kenedy Tabah Tanko
- Catalan
Institute of Nanoscience and Nanotechnology (ICN2), CSIC
and The Barcelona Institute of Science and Technology, 08193 Bellaterra, Barcelona, Spain
| | - Masoud Karimipour
- Catalan
Institute of Nanoscience and Nanotechnology (ICN2), CSIC
and The Barcelona Institute of Science and Technology, 08193 Bellaterra, Barcelona, Spain
| | - Aziz Ben Ali
- Department
of Electrical Engineering, School of Computation, Information and
Technology, Technical University of Munich, Hans-Piloty Strasse 1, 85748 Garching bei Munich,Germany
| | - D. Kishore Kumar
- Ben-Gurion
Solar Energy Center, Swiss Inst. for Dryland Environmental and Energy
Research, The Jacob Blaustein Institutes for Desert Research (BIDR), Ben-Gurion University of the Negev, Sede Boker Campus, Midereshet Ben-Gurion 84990, Israel
| | - Vediappan Sudhakar
- Ben-Gurion
Solar Energy Center, Swiss Inst. for Dryland Environmental and Energy
Research, The Jacob Blaustein Institutes for Desert Research (BIDR), Ben-Gurion University of the Negev, Sede Boker Campus, Midereshet Ben-Gurion 84990, Israel
| | - Ritesh Kant Gupta
- Ben-Gurion
Solar Energy Center, Swiss Inst. for Dryland Environmental and Energy
Research, The Jacob Blaustein Institutes for Desert Research (BIDR), Ben-Gurion University of the Negev, Sede Boker Campus, Midereshet Ben-Gurion 84990, Israel
| | - Iris Visoly-Fisher
- Ben-Gurion
Solar Energy Center, Swiss Inst. for Dryland Environmental and Energy
Research, The Jacob Blaustein Institutes for Desert Research (BIDR), Ben-Gurion University of the Negev, Sede Boker Campus, Midereshet Ben-Gurion 84990, Israel
| | - Monica Lira-Cantu
- Catalan
Institute of Nanoscience and Nanotechnology (ICN2), CSIC
and The Barcelona Institute of Science and Technology, 08193 Bellaterra, Barcelona, Spain
| | - Alessio Gagliardi
- Department
of Electrical Engineering, School of Computation, Information and
Technology, Technical University of Munich, Hans-Piloty Strasse 1, 85748 Garching bei Munich,Germany
- Munich
Data Science Institute, TUM, 85748 Garching, Walther-von-Dyck-Straße 10, Germany
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2
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Blaskovits JT, Laplaza R, Vela S, Corminboeuf C. Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305602. [PMID: 37815223 DOI: 10.1002/adma.202305602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 09/05/2023] [Indexed: 10/11/2023]
Abstract
The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.
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Affiliation(s)
- J Terence Blaskovits
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Competence in Research "Sustainable chemical processes through catalysis (NCCR Catalysis)" École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
| | - Sergi Vela
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL),Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
| | - Clémence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fedéralé de Lausanne (EPFL), Lausanne, 1015, Switzerland
- National Centre for Competence in Research "Sustainable chemical processes through catalysis (NCCR Catalysis)" École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL),Ecole Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland
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Lampe C, Kouroudis I, Harth M, Martin S, Gagliardi A, Urban AS. Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208772. [PMID: 36681859 DOI: 10.1002/adma.202208772] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/18/2023] [Indexed: 06/17/2023]
Abstract
With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2-6-monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions' quality.
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Affiliation(s)
- Carola Lampe
- Nanospectroscopy Group and Center for NanoScience, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universität München, 80539, Munich, Germany
| | - Ioannis Kouroudis
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748, Garching bei München, Germany
| | - Milan Harth
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748, Garching bei München, Germany
| | - Stefan Martin
- Nanospectroscopy Group and Center for NanoScience, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universität München, 80539, Munich, Germany
| | - Alessio Gagliardi
- Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Straße 1, 85748, Garching bei München, Germany
| | - Alexander S Urban
- Nanospectroscopy Group and Center for NanoScience, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universität München, 80539, Munich, Germany
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Ghosh R, Paesani F. Connecting the dots for fundamental understanding of structure-photophysics-property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism. Chem Sci 2023; 14:1040-1064. [PMID: 36756323 PMCID: PMC9891456 DOI: 10.1039/d2sc03793a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022] Open
Abstract
Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications.
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Affiliation(s)
- Raja Ghosh
- Department of Chemistry and Biochemistry, University of California La Jolla San Diego California 92093 USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California La Jolla San Diego California 92093 USA
- San Diego Supercomputer Center, University of California La Jolla San Diego California 92093 USA
- Materials Science and Engineering, University of California La Jolla San Diego California 92093 USA
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5
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Jia H, Jiang H, Chen Z, Feng Z, Zhang X, Zhang Y, Xu X, Li X, Peng F, Liu X, Qiu J. Near-Infrared Light-Induced Photoresponse in Er 3+/Li +-Codoped Y 2O 3/Poly(methyl methacrylate) Composite Film. J Phys Chem Lett 2022; 13:3470-3478. [PMID: 35416674 DOI: 10.1021/acs.jpclett.2c00713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We demonstrate Li+-doping engineering for improving the near-infrared (NIR) photoresponse in an Er3+-activated Y2O3 phosphor. We show that the rational incorporation of Li+ results in a large enhancement of the upconversion (UC) emission intensity up to 29 times upon excitation of NIR light. The improved UC properties could be associated with the enhanced dipole-dipole transition probability due to Li+-induced changes in the local site symmetry for Er3+ ions and improvement in the crystallinity of the samples. We further demonstrate the construction of UC phosphor/polymer composite films by attaching the UC phosphor/polymer composite film onto a Si-photoresistor. The device shows a large enhancement of the photovoltage response from 0.16 to 0.4 V in Li-doped samples under NIR light illumination. These results suggest an effective doping strategy for the improvement of the UC performance of the oxide phosphor and its wide applications in solar energy utilization and NIR response devices.
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Affiliation(s)
- Hong Jia
- College of Physics and Electronic Information & Key Laboratory of Electromagnetic Transformation and Detection of Henan province, Luoyang Normal University, Luoyang 471934, China
| | - Hongming Jiang
- School of Physics and Electronic Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
| | - Zhi Chen
- Zhejiang Lab, Hangzhou 311100, China
| | - Zhenyi Feng
- College of Physics and Electronic Information & Key Laboratory of Electromagnetic Transformation and Detection of Henan province, Luoyang Normal University, Luoyang 471934, China
| | - Xian Zhang
- College of Physics and Electronic Information & Key Laboratory of Electromagnetic Transformation and Detection of Henan province, Luoyang Normal University, Luoyang 471934, China
| | - Yuping Zhang
- College of Physics and Electronic Information & Key Laboratory of Electromagnetic Transformation and Detection of Henan province, Luoyang Normal University, Luoyang 471934, China
| | - Xiaoyun Xu
- Guangzhou City University of Technology, Guangzhou 510800, China
| | - Xue Li
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
| | - Feng Peng
- College of Physics and Electronic Information & Key Laboratory of Electromagnetic Transformation and Detection of Henan province, Luoyang Normal University, Luoyang 471934, China
| | - Xiaofeng Liu
- School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jianrong Qiu
- College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
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