Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neeraj Tomar, Geeta Rani, Vijaypal Singh Dhaka, Praveen K. Surolia, Kalpit Gupta, Eugenio Vocaturo, Ester Zumpano
{"title":"Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model","authors":"Neeraj Tomar, Geeta Rani, Vijaypal Singh Dhaka, Praveen K. Surolia, Kalpit Gupta, Eugenio Vocaturo, Ester Zumpano","doi":"10.3390/bdcc7020115","DOIUrl":null,"url":null,"abstract":"The exponentially growing energy requirements and, in turn, extensive depletion of non-restorable sources of energy are a major cause of concern. Restorable energy sources such as solar cells can be used as an alternative. However, their low efficiency is a barrier to their practical use. This provokes the research community to design efficient solar cells. Based on the study of efficacy, design feasibility, and cost of fabrication, DSSC shows supremacy over other photovoltaic solar cells. However, fabricating DSSC in a laboratory and then assessing their characteristics is a costly affair. The researchers applied techniques of computational chemistry such as Time-Dependent Density Functional Theory, and an ab initio method for defining the structure and electronic properties of dyes without synthesizing them. However, the inability of descriptors to provide an intuitive physical depiction of the effect of all parameters is a limitation of the proposed approaches. The proven potential of neural network models in data analysis, pattern recognition, and object detection motivated researchers to extend their applicability for predicting the absorption maxima (λmax) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λmax for inorganic ruthenium complex dyes used in DSSC. Furthermore, it demonstrates the impact of different activation functions, optimizers, and loss functions on the prediction accuracy of λmax. Moreover, this research showcases the impact of atomic weight, types of bonds between constituents of the dye molecule, and the molecular weight of the dye molecule on the value of λmax. The experimental results proved that the value of λmax varies with changes in constituent atoms and types of bonds in a dye molecule. In addition, the model minimizes the difference in the experimental and calculated values of absorption maxima. The comparison with the existing models proved the dominance of the proposed model.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":"18 1","pages":"0"},"PeriodicalIF":3.7000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/bdcc7020115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The exponentially growing energy requirements and, in turn, extensive depletion of non-restorable sources of energy are a major cause of concern. Restorable energy sources such as solar cells can be used as an alternative. However, their low efficiency is a barrier to their practical use. This provokes the research community to design efficient solar cells. Based on the study of efficacy, design feasibility, and cost of fabrication, DSSC shows supremacy over other photovoltaic solar cells. However, fabricating DSSC in a laboratory and then assessing their characteristics is a costly affair. The researchers applied techniques of computational chemistry such as Time-Dependent Density Functional Theory, and an ab initio method for defining the structure and electronic properties of dyes without synthesizing them. However, the inability of descriptors to provide an intuitive physical depiction of the effect of all parameters is a limitation of the proposed approaches. The proven potential of neural network models in data analysis, pattern recognition, and object detection motivated researchers to extend their applicability for predicting the absorption maxima (λmax) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λmax for inorganic ruthenium complex dyes used in DSSC. Furthermore, it demonstrates the impact of different activation functions, optimizers, and loss functions on the prediction accuracy of λmax. Moreover, this research showcases the impact of atomic weight, types of bonds between constituents of the dye molecule, and the molecular weight of the dye molecule on the value of λmax. The experimental results proved that the value of λmax varies with changes in constituent atoms and types of bonds in a dye molecule. In addition, the model minimizes the difference in the experimental and calculated values of absorption maxima. The comparison with the existing models proved the dominance of the proposed model.
基于分子结构的染料吸收最大值的ANN模型预测
能源需求呈指数级增长,而不可恢复的能源又大量耗竭,这是令人关切的主要原因。可再生能源,如太阳能电池,可以作为一种替代品。然而,它们的低效率阻碍了它们的实际应用。这促使研究界设计出高效的太阳能电池。基于效能、设计可行性和制造成本的研究,DSSC显示出优于其他光伏太阳能电池的优势。然而,在实验室中制造DSSC,然后评估它们的特性是一件昂贵的事情。研究人员应用了计算化学技术,如时间依赖密度泛函理论,以及一种从头算方法来定义染料的结构和电子性质,而不需要合成它们。然而,描述符无法提供所有参数影响的直观物理描述是所提出方法的一个限制。神经网络模型在数据分析、模式识别和目标检测方面的潜力已被证明,这促使研究人员将其应用于预测染料的吸收最大值(λmax)。本研究的目的是建立一个基于人工神经网络的QSPR模型,以正确预测用于DSSC的无机钌络合染料的λmax值。进一步证明了不同的激活函数、优化器和损失函数对λmax预测精度的影响。此外,本研究还展示了染料分子的原子量、各组分之间的键类型以及染料分子的分子量对λmax值的影响。实验结果表明,λmax值随染料分子组成原子和化学键类型的变化而变化。此外,该模型将吸收最大值的实验值与计算值之间的差异最小化。通过与已有模型的比较,证明了该模型的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信