Recent advances of computational chemistry in organic solar cell research

Cui, Yongjie; Zhu, Peipei; Liao, Xunfan; Chen, Yiwang

doi:10.1039/d0tc03709e

Cited by 74 publications

(69 citation statements)

References 167 publications

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“…Some other recent reviews with the same context can also be found in the literature. [ 41,50,51 ] Every single occurrence in the device can be explained from a quantum level. We start by looking at an atom and its quantum mechanisms.…”

Section: The Structure–morphology–performance Theoretical Frameworkmentioning

confidence: 99%

Unraveling the Mystery of Ternary Organic Solar Cells: A Review on the Influence of Third Component on Structure–Morphology–Performance Relationships

Tan

Wong

2021

Solar RRL

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The addition of a third component to a binary bulk heterojunction configuration in organic solar cells is called ternary organic solar cells (TOSCs), which is trending as a solar cell configuration that strikes balance between tandem and binary bulk heterojunction organic solar cells by increasing spectral absorption without laborious processing methods. Apart from increasing the spectral range, the third component is also able to tune the morphology and electronic properties for enhanced performance. Existing reviews on TOSCs thus far mainly focus on reporting related research progresses and their respective performances. Instead, a thorough review on understanding the influence of the third component in terms of morphology and electronic properties via a computational chemistry perspective (structure–morphology–performance) is presented here. This bottom‐up approach reviews the influence of the third component originating from intermolecular interactions to crystallization and phase properties, and finally to electronic properties such as charge transfer and energy transfer. Herein, new theoretical insights opening up potential research opportunities to propel organic solar cells to one day surpass 20% power conversion efficiency are revealed.

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Section: The Structure–morphology–performance Theoretical Frameworkmentioning

confidence: 99%

Unraveling the Mystery of Ternary Organic Solar Cells: A Review on the Influence of Third Component on Structure–Morphology–Performance Relationships

Tan

Wong

2021

Solar RRL

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“…DL methods have also been exploited in predicting ground- and excited state properties for thousands of organic molecules, where the accuracy for small molecules can be even superior to QM ab initio methods [ 78 ]. Recent advances in the use of machine learning and computational chemistry methods to study organic photovoltaics are discussed in other works [ 80 , 81 , 82 ].…”

Section: Materials Discoverymentioning

confidence: 99%

Big data and machine learning for materials science

et al. 2021

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Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

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“…In order to approach to the theoretical limit of OSCs, strenuous research efforts had been made in these three aspects: (1) design and synthesize novel donor and acceptor materials, (2) optimize fabrication conditions and device structures, and (3) explore the mechanism of device operation. [ 10 ] The first two methods are a trial‐and‐error procedure, which requires expensive materials, long‐time consumption, and large manpower input while the last manner is a new way to study the physicochemical properties of materials and to simulate the device operation processes only based on multiscale computational methods, such as Density Function Theory (DFT), Molecular Dynamics (MD), Monte Carlo (MC) and so on, [ 11–17 ] which provides helpful guidelines and insight for designing novel molecules. In principles, the final device performance is only dependent on its materials and the function of manufacturing technology and device structures is to better give play to the role of materials.…”

Section: Introductionmentioning

confidence: 99%

Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

Feng

Wang

et al. 2021

Nano Select

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Over past two decades, organic photovoltaics (OPVs) with unique advantages of low cost and flexibility meet significant development opportunities and the official world record for the power conversion efficiency (PCE) of organic solar cells (OSCs) has reached to 17.3%. Traditionally, efficiency breakthrough need the constant input of intensive labor and time. The artificial intelligence, as a rising interdisciplinary, brings certainly a revolution in research methods. In this review, we introduce a state‐of‐art theoretical methodology of the synergy of high‐throughput screening and machine learning (ML) in accelerating the discovery of high‐efficient OSC materials. We present key details, rules and experience in database construction, selection of molecular features, fast‐screening calculations, models training and their predication capabilities. Meanwhile, three typical ML frameworks are concluded to reveal the structure‐property‐efficiency relationship, suggesting that this theoretical methodology can train powerful models with just molecular configurations and theoretical calculations for molecular design and efficiency improvements.

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Recent advances of computational chemistry in organic solar cell research

Abstract: The precise design of organic photovoltaic materials and the control of morphology in active layer are crucial for achieving high-performance organic solar cells (OSCs). Up to now, however, it remains...

Cited by 74 publications

References 167 publications

Unraveling the Mystery of Ternary Organic Solar Cells: A Review on the Influence of Third Component on Structure–Morphology–Performance Relationships

Unraveling the Mystery of Ternary Organic Solar Cells: A Review on the Influence of Third Component on Structure–Morphology–Performance Relationships

Big data and machine learning for materials science

Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

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