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Database for design of solar cell active layer through genetic algorithm

Main Author: Ardayfio, Caine
Format: Dataset
Terbitan: Mendeley , 2019
Subjects:
Materials Science
Solar Cell
Genetic Algorithm
Energy Application
Online Access: https:/data.mendeley.com/datasets/rvdnt639c2
ctrlnum 0.17632-rvdnt639c2.2
fullrecord <?xml version="1.0"?> <dc><creator>Ardayfio, Caine</creator><title>Database for design of solar cell active layer through genetic algorithm</title><publisher>Mendeley</publisher><description>Microstructure design is a crucial part of developing organic solar cells. Organic solar cells have the potential to become ubiquitous amongst power generation due to their inexpensiveness and ease of fabrication. Although achievements in the chemical properties of the solar cells have been achieved in recent years, a lack of progress in morphology has greatly inhibited organic solar cell adoption. In this, it is illustrated how high-performance microstructures can be developed rapidly via a graph-based strategy. This is in stark contrast to the trial-and-error methods currently employed for organic solar cell microstructure optimization. Treating the microstructure of a material system as graphs allows modular and extensible models that are simple to query and evaluate. The graph surrogate model quickly maps the microstructures properties and integrates well with optimization algorithms while elegantly integrating prior domain knowledge into the microstructure design process. This use of graph-based modeling and probabilistic optimization results in a microstructure design with a 40.29% higher efficiency than conventional solar designs. Fractal analysis was also used to further prove the validity of the designed morphologies. This was accomplished through analyzing models analogous to the function of the solar cell and comparing their similarity with the designed fractal structure. To conclude, graph-based probabilistic optimization led to the identification of a class of microstructures that feature significantly higher efficiencies than currently leading solar cells. It is anticipated that coupling this method with fractal analysis techniques will be widespread for use in optimizing material morphologies. The following dataset includes all code used in microstructure design and fractal analysis, specifically: the creation of a weighted, undirected graph representing the microstructure configuration of chemicals in the solar cell; the approximation of solar cell efficiency through graph-based querying; the optimization of the system through a probabilistic genetic algorithm; and fractal dimension calculator. </description><subject>Materials Science</subject><subject>Solar Cell</subject><subject>Genetic Algorithm</subject><subject>Energy Application</subject><type>Other:Dataset</type><identifier>10.17632/rvdnt639c2.2</identifier><rights>Attribution-NonCommercial 3.0 Unported</rights><rights>https://creativecommons.org/licenses/by-nc/3.0</rights><relation>https:/data.mendeley.com/datasets/rvdnt639c2</relation><date>2019-07-26T05:48:41Z</date><recordID>0.17632-rvdnt639c2.2</recordID></dc>
format Other:Dataset
Other
author Ardayfio, Caine
title Database for design of solar cell active layer through genetic algorithm
publisher Mendeley
publishDate 2019
topic Materials Science
Solar Cell
Genetic Algorithm
Energy Application
url https:/data.mendeley.com/datasets/rvdnt639c2
contents Microstructure design is a crucial part of developing organic solar cells. Organic solar cells have the potential to become ubiquitous amongst power generation due to their inexpensiveness and ease of fabrication. Although achievements in the chemical properties of the solar cells have been achieved in recent years, a lack of progress in morphology has greatly inhibited organic solar cell adoption. In this, it is illustrated how high-performance microstructures can be developed rapidly via a graph-based strategy. This is in stark contrast to the trial-and-error methods currently employed for organic solar cell microstructure optimization. Treating the microstructure of a material system as graphs allows modular and extensible models that are simple to query and evaluate. The graph surrogate model quickly maps the microstructures properties and integrates well with optimization algorithms while elegantly integrating prior domain knowledge into the microstructure design process. This use of graph-based modeling and probabilistic optimization results in a microstructure design with a 40.29% higher efficiency than conventional solar designs. Fractal analysis was also used to further prove the validity of the designed morphologies. This was accomplished through analyzing models analogous to the function of the solar cell and comparing their similarity with the designed fractal structure. To conclude, graph-based probabilistic optimization led to the identification of a class of microstructures that feature significantly higher efficiencies than currently leading solar cells. It is anticipated that coupling this method with fractal analysis techniques will be widespread for use in optimizing material morphologies. The following dataset includes all code used in microstructure design and fractal analysis, specifically: the creation of a weighted, undirected graph representing the microstructure configuration of chemicals in the solar cell; the approximation of solar cell efficiency through graph-based querying; the optimization of the system through a probabilistic genetic algorithm; and fractal dimension calculator.
id IOS7969.0.17632-rvdnt639c2.2
institution Universitas Islam Indragiri
affiliation onesearch.perpusnas.go.id
institution_id 804
institution_type library:university
library
library Teknologi Pangan UNISI
library_id 2816
collection Artikel mulono
repository_id 7969
city INDRAGIRI HILIR
province RIAU
shared_to_ipusnas_str 1
repoId IOS7969
first_indexed 2020-04-08T08:23:07Z
last_indexed 2020-04-08T08:23:07Z
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