Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model
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 provo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/7/2/115 |
| _version_ | 1797596084707524608 |
|---|---|
| author | Neeraj Tomar Geeta Rani Vijaypal Singh Dhaka Praveen K. Surolia Kalpit Gupta Eugenio Vocaturo Ester Zumpano |
| author_facet | Neeraj Tomar Geeta Rani Vijaypal Singh Dhaka Praveen K. Surolia Kalpit Gupta Eugenio Vocaturo Ester Zumpano |
| author_sort | Neeraj Tomar |
| collection | DOAJ |
| description | 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 (λ<sub>max</sub>) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λ<sub>max</sub> 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 λ<sub>max</sub>. 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 λ<sub>max</sub>. The experimental results proved that the value of λ<sub>max</sub> 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. |
| first_indexed | 2024-03-11T02:45:33Z |
| format | Article |
| id | doaj.art-3b5c30b50f614fc1acab2e745e5174a5 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-11T02:45:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-3b5c30b50f614fc1acab2e745e5174a52023-11-18T09:18:57ZengMDPI AGBig Data and Cognitive Computing2504-22892023-06-017211510.3390/bdcc7020115Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN ModelNeeraj Tomar0Geeta Rani1Vijaypal Singh Dhaka2Praveen K. Surolia3Kalpit Gupta4Eugenio Vocaturo5Ester Zumpano6Department of Chemistry, Manipal University Jaipur, Jaipur 303007, IndiaDepartment of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, IndiaDepartment of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, IndiaDepartment of Chemistry, Manipal University Jaipur, Jaipur 303007, IndiaDepartment of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, IndiaDepartment of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, ItalyDepartment of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, ItalyThe 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 (λ<sub>max</sub>) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λ<sub>max</sub> 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 λ<sub>max</sub>. 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 λ<sub>max</sub>. The experimental results proved that the value of λ<sub>max</sub> 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.https://www.mdpi.com/2504-2289/7/2/115solarDSSCartificial neural networkenergyλ<sub>max</sub> |
| spellingShingle | Neeraj Tomar Geeta Rani Vijaypal Singh Dhaka Praveen K. Surolia Kalpit Gupta Eugenio Vocaturo Ester Zumpano Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model Big Data and Cognitive Computing solar DSSC artificial neural network energy λ<sub>max</sub> |
| title | Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model |
| title_full | Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model |
| title_fullStr | Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model |
| title_full_unstemmed | Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model |
| title_short | Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model |
| title_sort | molecular structure based prediction of absorption maxima of dyes using ann model |
| topic | solar DSSC artificial neural network energy λ<sub>max</sub> |
| url | https://www.mdpi.com/2504-2289/7/2/115 |
| work_keys_str_mv | AT neerajtomar molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT geetarani molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT vijaypalsinghdhaka molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT praveenksurolia molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT kalpitgupta molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT eugeniovocaturo molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel AT esterzumpano molecularstructurebasedpredictionofabsorptionmaximaofdyesusingannmodel |