Accelerating organic solar cell material's discovery: high-throughput screening and
The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becomi...
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| Published in | Energy & environmental science Vol. 14; no. 6; pp. 331 - 3322 |
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| Main Authors | , , |
| Format | Journal Article |
| Published |
16.06.2021
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| Online Access | Get full text |
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| Summary: | The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhance
big data
readiness. Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure-activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics.
This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms. |
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| Bibliography: | 10.1039/d1ee00559f Xabier Rodríguez-Martínez completed his bachelor and master's degrees in Nanoscience and Nanotechnology at the Autonomous University of Barcelona in 2015. In early 2016, he enrolled as PhD candidate in the Nanostructured Materials for Optoelectronics and Energy Harvesting (NANOPTO) group at ICMAB-CSIC, supported by Dr Mariano Campoy-Quiles as research supervisor. In October 2020, he obtained his PhD in materials science for the development of organic solar cells by high-throughput combinatorial methods. His main research interests focus on organic electronics for energy harvesting applications, including thin film processing, spectroscopy, and applied machine-learning methods. He has recently started a postdoctoral position at Linköping University. Mariano Campoy-Quiles and his team at the Institute of Materials Science of Barcelona, focus their research efforts on the understanding and development of organic and hybrid materials for energy and optoelectronic applications, and in particular, through photovoltaic (light-to-electricity) and thermoelectric (heat-to-electricity) technologies. He has been the recipient of a number prestigious of awards, including the Spanish Royal Society of Physics Young Researcher Award in Experimental Physics and an ERC Consolidator Grant by the European Research Council. He is currently the Coordinator of the Materials for Energy and Environment of the main Spanish research funding agency (AEI). Enrique Pascual received his bachelor and master's degree in Mechanical Engineering at the University of Malaga (Spain) in 2015. After working for a year at Abengoa Research, he joined a research project between Dr Mariano Campoy-Quiles' group at ICMAB-CSIC and The Technology Centre of Catalonia-EURECAT focusing on upscaling routes for solution-processable organic photovoltaic technologies. In June 2020, he obtained his PhD in Materials Science at the Autonomous University Barcelona. His research interests are mainly focused on accelerating lab-to-fab transition with the aid of high-throughput screening methodologies. He has recently joined the University of Malaga as lecturer. Electronic supplementary information (ESI) available. See DOI |
| ISSN: | 1754-5692 1754-5706 |
| DOI: | 10.1039/d1ee00559f |