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

Nano Select ◽  
2021 ◽  
Author(s):  
Jie Feng ◽  
Hongshuai Wang ◽  
Yujin Ji ◽  
Youyong Li
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Performance Improvement ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Molecular Design ◽  
And Performance

High Throughput Screening of Highly Efficient Non-fullerene Acceptor based Organic Solar Cells Assisted by a Multi-Dataset Scientific Robot

2019 ◽  
Author(s):  
Enrique Pascual-San-José ◽  
Xabier Rodríguez-Martínez ◽  
Fei Zhuping ◽  
Martin Heeney ◽  
Roger Guimerà-Manrique ◽  
...  
Keyword(s):  
Solar Cells ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Highly Efficient

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

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.

The Effect of Donor Molecular Structure on Power Conversion Efficiency of Small-Molecule-Based Organic Solar Cells

2019 ◽  
Vol 16 (3) ◽  
pp. 236-243 ◽  
Author(s):  
Hui Zhang ◽  
Yibing Ma ◽  
Youyi Sun ◽  
Jialei Liu ◽  
Yaqing Liu ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Solar Cells ◽  
Power Conversion Efficiency ◽  
Conversion Efficiency ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Molecular Design ◽  
Power Conversion ◽  
The Relationship

In this review, small-molecule donors for application in organic solar cells reported in the last three years are highlighted. Especially, the effect of donor molecular structure on power conversion efficiency of organic solar cells is reported in detail. Furthermore, the mechanism is proposed and discussed for explaining the relationship between structure and power conversion efficiency. These results and discussions draw some rules for rational donor molecular design, which is very important for further improving the power conversion efficiency of organic solar cells based on the small-molecule donor.

scholarly journals Molecular design revitalizes the low-cost PTV-polymer for highly efficient organic solar cells

2021 ◽  
Author(s):  
Junzhen Ren ◽  
Pengqing Bi ◽  
Jianqi Zhang ◽  
Jiao Liu ◽  
Jingwen Wang ◽  
...  
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
High Performance ◽  
Molecular Design ◽  
Raw Materials ◽  
Low Cost ◽  
Stable Conformation ◽  
Chemical Structures ◽  
Aggregation Effect ◽  
Industrialization Process

Abstract Developing photovoltaic materials with simple chemical structures and easy synthesis still remains a major challenge in the industrialization process of organic solar cells (OSCs). Herein, an ester substituted poly(thiophene vinylene) derivative, PTVT-T, was designed and synthesized in very few steps by adopting commercially available raw materials. The ester groups on the thiophene units enable PTVT-T to have a planar and stable conformation. Moreover, PTVT-T presents a wide absorption band and strong aggregation effect in solution, which are the key characteristics needed to realize high performance in non-fullerene-acceptor (NFA)-based OSCs. We then prepared OSCs by blending PTVT-T with three representative fullerene- and NF-based acceptors, PC71BM, IT-4F and BTP-eC9. It was found that PTVT-T can work well with all the acceptors, showing great potential to match new emerging NFAs. Particularly, a remarkable power conversion efficiency of 16.20% is achieved in a PTVT-T:BTP-eC9-based device, which is the highest value among the counterparts based on PTV derivatives. This work demonstrates that PTVT-T shows great potential for the future commercialization of OSCs.

scholarly journals Synthesis and Theoretical Investigation Using DFT of 2,3-Diphenylquinoxaline Derivatives for Electronic and Photovoltaic Effects

2021 ◽  
Author(s):  
Dorota Zając ◽  
Dariusz Przybylski ◽  
Jadwiga Sołoducho
Keyword(s):  
Solar Cells ◽  
Acrylic Acid ◽  
Organic Semiconductors ◽  
Organic Solar Cells ◽  
Molecular Design ◽  
Low Cost ◽  
Design Synthesis ◽  
Semiconducting Properties ◽  
Homo Lumo

AbstractDeveloping effective and low‐cost organic semiconductors is an opportunity for the development of organic solar cells (OPV). Herein, we report the molecular design, synthesis and characterization of two molecules with D–A–D–A configuration: 2-cyano-3-(5-(8-(3,4-ethylenodioxythiophen-5-yl)-2,3-diphenylquinoxalin-5-yl)thiophen-2-yl)acrylic acid (6) and 2-cyano-3-(5-(2,3-diphenyl-8-(thiophen-2-yl)quinoxalin-5-yl)thiophen-2-yl)acrylic acid (7). Moreover, we investigated the structural, theoretical and optical properties. The distribution of HOMO/LUMO orbitals and the values of the ionization potential indicate good semiconducting properties of the compounds and that they can be a bipolar material. Also, the optical study show good absorption in visible light (λabs 380–550 nm). We investigate the theoretical optoelectronic properties of obtained compounds as potential materials for solar cells.

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

2022 ◽  
Author(s):  
Jin-Liang Wang ◽  
Asif Mahmood ◽  
Ahmad Irfan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Solar Cells ◽  
Molecular Dynamics Simulations ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Evolutionary Design ◽  
New Materials ◽  
Dynamics Simulations ◽  
Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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