*Result*: Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs).
Title:
Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs).
Authors:
Pore S; Drug Theoretics and Chemoinformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India., Banerjee A; Drug Theoretics and Chemoinformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India., Roy K; Drug Theoretics and Chemoinformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India.
Source:
Molecular informatics [Mol Inform] 2024 Apr; Vol. 43 (4), pp. e202300210. Date of Electronic Publication: 2024 Feb 19.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-VCH Verlag Country of Publication: Germany NLM ID: 101529315 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1868-1751 (Electronic) Linking ISSN: 18681743 NLM ISO Abbreviation: Mol Inform Subsets: MEDLINE
Imprint Name(s):
Original Publication: Weinheim, Germany : Wiley-VCH Verlag
MeSH Terms:
References:
W. H. Chen, F. You, Renew. Sustain. Energy Rev. 2022, 168, 112830.
T. Z. Ang, M. Salem, M. Kamarol, H. S. Das, M. A. Nazari, N. Prabaharan, Energy Strategy Rev. 2022, 43, 100939.
S. Sharma, K. K. Jain, A. Sharma, Mater. Sci. Appl. 2015, 06, 1145–1155.
G. N. Arka, S. B. Prasad, S. Singh, Sol. Energy 2021, 226, 192–213.
K. Sharma, V. Sharma, S. S. Sharma, Nanoscale Res. Lett. 2018, 13, 1–46.
A. Hagfeldt, G. Boschloo, L. Sun, L. Kloo, H. Pettersson, Chem. Rev. 2010, 110, 6595–6663.
A. Baheti, K. R. J. Thomas, C. T. Li, C. P. Lee, K. C. Ho, ACS Appl. Mater. Interfaces 2015, 7, 2249–2262.
M. Pastore, S. Fantacci, F. De Angelis, J. Phys. Chem. C 2013, 117, 3685–3700.
S. Kar, J. K. Roy, J. Leszczynski, Npj Comput. Mater. 2017, 31, 1–12.
Y. Wen, L. Fu, G. Li, J. Ma, H. Ma, Sol. RRL 2020, 4, 2000110.
G. R. Kandregula, D. K. Murugaiah, N. A. Murugan, K. Ramanujam, New J. Chem. 2022, 9, 4395–4405.
A. Kumar, P. Kumar, SAR QSAR Environ. Res. 2021, 32, 817–834.
H. Li, Y. Cui, Y. Liu, W. Li, Y. Shi, C. Fang, H. Li, T. Gao, L. Hu, Y. Lu, IEEE Access 2018, 6, 34118–34126.
J. G . Krishna, P. K. Ojha, S. Kar, K. Roy, J. Leszczynski, Nano Energy 2020, 70, 104537.
J. M. Cole, U. F. Mayer, Langmuir 2022, 38, 871–890.
V. Venkatraman, S. Abburu, B. K. Alsberg, Phys. Chem. Chem. Phys. 2015, 17, 27672–27682.
H. Li, Z. Zhong, L. Li, R. Gao, J. Cui, T. Gao, L. H. Hu, Y. Lu, Z. M. Su, H. Li, J. Comput. Chem. 2015, 36, 1036–1046.
V. Venkatraman, M. Foscato, V. R. Jensen, B. K. Alsberg, J. Mater. Chem. A. 2015, 3, 9851–9860.
V. Venkatraman, B. K. Alsberg, Dye Pigment 2015, 114, 69–77.
S. Ramakrishna, T. Y. Zhang, W. C. Lu, Q. Qian, J. S. C. Low, J. H. R. Yune, D. Z. L. Tan, S. Bressan, S. Sanvito, S. R. Kalidindi, J. Intell. Manuf. 2019, 30, 2307–2326.
K. Takahashi, Y. Tanaka, Dalton Trans. 2016, 45, 10497–10499.
A. Agrawal, A. Choudhary, MRS Commun. 2019, 9, 779–792.
A. Lopez-Bezanilla, P. B. Littlewood, MRS Commun. 2020, 10, 1–10.
L. Zhang, J. Miao, J. Li, Q. Li, Adv. Funct. Mater. 2020, 30, 2003653.
L. S. Zhang, X. L. Gao, X. H. Liu, Z. J. Zhang, R. Cao, H. C. Cheng, M. Y. Wang, X. Y. Yan, S. C. Yang, Rare Met. 2022, 41, 1477–1489.
N. Kireeva, V. S. Pervov, Batter. Supercaps 2020, 3, 427–438.
L. Cheng, R. S. Assary, X. Qu, A. Jain, S. P. Ong, N. N. Rajput, K. Persson, L. A. Curtiss, J. Phys. Chem. Lett. 2015, 6, 283–291.
K. Takahashi, L. Takahashi, in Machine Learning in Materials Informatics: Methods and Applications (Ed.: Y. An), American Chemical Society, Washington, D. C. 2022.
K. Takahashi, L. Takahashi, I. Miyazato, J. Fujima, Y. Tanaka, T. Uno, H. Satoh, K. Ohno, M. Nishida, K. Hirai, J. Ohyama, ChemCatChem 2019, 11, 1146–1152.
A. Saeki, K. Kranthiraja, Jpn. J. Appl. Phys. 2019, 59, 0801.
L. E. Vivanco-Benavides, C. L. Martínez-González, C. Mercado-Zúñiga, C. Torres-Torres, Comput. Mater. Sci. 2022, 201, 110939.
S. R. Kalidindi, D. B. Brough, S. Li, A. Cecen, A. L. Blekh, F. Y. P. Congo, C. Campbell, MRS Bull. 2016, 41, 596–602.
M. I. Jordan, T. M. Mitchell, Sci. 2015, 349, 255–260.
A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed., O'Reilly Media, Inc., USA, 2022.
Y. C. Lo, S. E. Rensi, W. Torng, R. B. Altman, Drug Discov. Today 2018, 23, 1538–1546.
P. Linardatos, V. Papastefanopoulos, S. Kotsiantis, Entropy 2020, 23, 18.
K. Roy, S. Kar, R. N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press, NY, 2015.
E. N. Muratov, J. Bajorath, R. P. Sheridan, I. V. Tetko, D. Filimonov, V. Poroikov, T. I. Oprea, I. L. Baskin, A. Varnek, A. Roitberg, O. Isayev, Chem. Soc. Rev. 2020, 49, 3525–3564.
E. Berggren, P. Amcoff, R. Benigni, K. Blackburn, E. Carney, M. Cronin, H. Deluyker, F. Gautier, R. S. Judson, G. E. N. Kass, D. Keller, D. Knight, W. Lilienblum, C. Mahony, I. Rusyn, T. Schultz, M. Schwarz, G. Schüürmann, A. White, J. Burton, A. M. Lostia, S. Munn, A. Worth, Environ. Health Perspect. 2015, 123, 1232–1240.
A. Banerjee, M. Chatterjee, P. De, K. Roy, Chemom. Intell. Lab. Syst. 2022, 227, 104613.
T. Luechtefeld, D. Marsh, C. Rowlands, T. Hartung, Toxicol. Sci. 2018, 165, 198–212.
A. Banerjee, K. Roy, Mol. Divers. 2022, 26, 2847–2862.
A. Banerjee, A. Gajewicz-Skretna, K. Roy, Mol. Inform. 2023, 42, 2200261.
S. Ghosh, M. Chatterjee, K. Roy, J. Agric. Food Chem. 2023, 71, 9538–9548.
S. Pore, A. Banerjee, K. Roy, Sustain. Energy Fuels 2023, 7, 3412–3431.
V. Vishwesh, R. Rajesh, S. P. Oikonomopoulos, B. K. Alsberg, J. Cheminformatics 2018, 10, 1758–2946.
A. Mauri, alvaDesc: A tool to calculate and Analyze Molecular Descriptors and Fingerprints (Ed.: K. Roy), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, New York, 2020.
S. Wold, K. Esbensen, P. Geladi, Chemom. Intell. Lab. Syst. 1987, 2, 37–52.
P. Gramatica, Comput. Toxicol. 2013, 2, 499–526.
M. Goodarzi, B. Dejaegher, Y. V. Heyden, J. AOAC Int. 2012, 95, 636–651.
M. Shahlaei, Chem. Rev. 2013, 113, 8093–8103.
J. Cai, J. Luo, S. Wang, S. Yang, Neurocomputing 2018, 300, 70–79.
J. Devillers, Genetic Algorithms in Molecular Modeling, Academic Press, Lyon, France, 1996.
S. Wold, M. Sjöström, L. Eriksson, Chemom. Intell. Lab. Syst. 2001, 58, 109–130.
R. J. Chase, D. R. Harrison, A. Burke, G. M. Lackmann, A. McGovern, Weather Forecast 2022, 37, 1509–1529.
Z. Wu, D. Li, J. Meng, H. Wang, Introduction to SIMCA−P and Its Application (Ed.: V. Esposito Vinzi, W. Chin, J. Henseler, H. Wang), Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg, 2010.
S. M. Lundberg, S. I. Lee, Adv. Neural Inf. Process. Syst. 2017, 30, 4765–4774.
L. Shapley, in 7. A Value for n-Person Games. Contributions to the Theory of Games II (1953) 307–317, Classics in Game Theory (Ed.: H. W. Kuhn), Princeton University Press, Princeton, 1997, pp. 69–79.
A. Holzinger, A. Saranti, C. Molnar, P. Biecek, W. Samek, Explainable AI Methods - A Brief Overview (Ed.: A. Holzinger, R. Goebel, R. Fong, T. Moon, K R. Müller, W. Samek), xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science, vol. 13200, Springer, Cham. 2022.
R. Rodríguez-Pérez, J. Bajorath, J. Comput. Aided Mol. Des. 2020, 34, 1013–1026.
A. Moncada-Torres, M. C. van Maaren, M. P. Hendriks, S. Siesling, G. Geleijnse, Sci. Rep. 2021, 11, 6968.
A. Banerjee, K. Roy, Environ. Sci.: Processes Impacts 2023, 25, 1626–1644.
A. Banerjee, S. Kar, S. Pore, K. Roy, Nanotoxicology 2023, 17, 78–93.
G. Sun, P. Bai, T. Fan, L. Zhao, R. Zhong, R. S. McElhinney, T. B. H. McMurry, D. J. Donnelly, J. E. McCormick, J. Kelly, G. P. Margison, Pharmaceutics 2023, 15, 2170.
A. Banerjee, K. Roy, Chem. Res. Toxicol. 2023, 36, 1518–1531.
T. Z. Ang, M. Salem, M. Kamarol, H. S. Das, M. A. Nazari, N. Prabaharan, Energy Strategy Rev. 2022, 43, 100939.
S. Sharma, K. K. Jain, A. Sharma, Mater. Sci. Appl. 2015, 06, 1145–1155.
G. N. Arka, S. B. Prasad, S. Singh, Sol. Energy 2021, 226, 192–213.
K. Sharma, V. Sharma, S. S. Sharma, Nanoscale Res. Lett. 2018, 13, 1–46.
A. Hagfeldt, G. Boschloo, L. Sun, L. Kloo, H. Pettersson, Chem. Rev. 2010, 110, 6595–6663.
A. Baheti, K. R. J. Thomas, C. T. Li, C. P. Lee, K. C. Ho, ACS Appl. Mater. Interfaces 2015, 7, 2249–2262.
M. Pastore, S. Fantacci, F. De Angelis, J. Phys. Chem. C 2013, 117, 3685–3700.
S. Kar, J. K. Roy, J. Leszczynski, Npj Comput. Mater. 2017, 31, 1–12.
Y. Wen, L. Fu, G. Li, J. Ma, H. Ma, Sol. RRL 2020, 4, 2000110.
G. R. Kandregula, D. K. Murugaiah, N. A. Murugan, K. Ramanujam, New J. Chem. 2022, 9, 4395–4405.
A. Kumar, P. Kumar, SAR QSAR Environ. Res. 2021, 32, 817–834.
H. Li, Y. Cui, Y. Liu, W. Li, Y. Shi, C. Fang, H. Li, T. Gao, L. Hu, Y. Lu, IEEE Access 2018, 6, 34118–34126.
J. G . Krishna, P. K. Ojha, S. Kar, K. Roy, J. Leszczynski, Nano Energy 2020, 70, 104537.
J. M. Cole, U. F. Mayer, Langmuir 2022, 38, 871–890.
V. Venkatraman, S. Abburu, B. K. Alsberg, Phys. Chem. Chem. Phys. 2015, 17, 27672–27682.
H. Li, Z. Zhong, L. Li, R. Gao, J. Cui, T. Gao, L. H. Hu, Y. Lu, Z. M. Su, H. Li, J. Comput. Chem. 2015, 36, 1036–1046.
V. Venkatraman, M. Foscato, V. R. Jensen, B. K. Alsberg, J. Mater. Chem. A. 2015, 3, 9851–9860.
V. Venkatraman, B. K. Alsberg, Dye Pigment 2015, 114, 69–77.
S. Ramakrishna, T. Y. Zhang, W. C. Lu, Q. Qian, J. S. C. Low, J. H. R. Yune, D. Z. L. Tan, S. Bressan, S. Sanvito, S. R. Kalidindi, J. Intell. Manuf. 2019, 30, 2307–2326.
K. Takahashi, Y. Tanaka, Dalton Trans. 2016, 45, 10497–10499.
A. Agrawal, A. Choudhary, MRS Commun. 2019, 9, 779–792.
A. Lopez-Bezanilla, P. B. Littlewood, MRS Commun. 2020, 10, 1–10.
L. Zhang, J. Miao, J. Li, Q. Li, Adv. Funct. Mater. 2020, 30, 2003653.
L. S. Zhang, X. L. Gao, X. H. Liu, Z. J. Zhang, R. Cao, H. C. Cheng, M. Y. Wang, X. Y. Yan, S. C. Yang, Rare Met. 2022, 41, 1477–1489.
N. Kireeva, V. S. Pervov, Batter. Supercaps 2020, 3, 427–438.
L. Cheng, R. S. Assary, X. Qu, A. Jain, S. P. Ong, N. N. Rajput, K. Persson, L. A. Curtiss, J. Phys. Chem. Lett. 2015, 6, 283–291.
K. Takahashi, L. Takahashi, in Machine Learning in Materials Informatics: Methods and Applications (Ed.: Y. An), American Chemical Society, Washington, D. C. 2022.
K. Takahashi, L. Takahashi, I. Miyazato, J. Fujima, Y. Tanaka, T. Uno, H. Satoh, K. Ohno, M. Nishida, K. Hirai, J. Ohyama, ChemCatChem 2019, 11, 1146–1152.
A. Saeki, K. Kranthiraja, Jpn. J. Appl. Phys. 2019, 59, 0801.
L. E. Vivanco-Benavides, C. L. Martínez-González, C. Mercado-Zúñiga, C. Torres-Torres, Comput. Mater. Sci. 2022, 201, 110939.
S. R. Kalidindi, D. B. Brough, S. Li, A. Cecen, A. L. Blekh, F. Y. P. Congo, C. Campbell, MRS Bull. 2016, 41, 596–602.
M. I. Jordan, T. M. Mitchell, Sci. 2015, 349, 255–260.
A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed., O'Reilly Media, Inc., USA, 2022.
Y. C. Lo, S. E. Rensi, W. Torng, R. B. Altman, Drug Discov. Today 2018, 23, 1538–1546.
P. Linardatos, V. Papastefanopoulos, S. Kotsiantis, Entropy 2020, 23, 18.
K. Roy, S. Kar, R. N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press, NY, 2015.
E. N. Muratov, J. Bajorath, R. P. Sheridan, I. V. Tetko, D. Filimonov, V. Poroikov, T. I. Oprea, I. L. Baskin, A. Varnek, A. Roitberg, O. Isayev, Chem. Soc. Rev. 2020, 49, 3525–3564.
E. Berggren, P. Amcoff, R. Benigni, K. Blackburn, E. Carney, M. Cronin, H. Deluyker, F. Gautier, R. S. Judson, G. E. N. Kass, D. Keller, D. Knight, W. Lilienblum, C. Mahony, I. Rusyn, T. Schultz, M. Schwarz, G. Schüürmann, A. White, J. Burton, A. M. Lostia, S. Munn, A. Worth, Environ. Health Perspect. 2015, 123, 1232–1240.
A. Banerjee, M. Chatterjee, P. De, K. Roy, Chemom. Intell. Lab. Syst. 2022, 227, 104613.
T. Luechtefeld, D. Marsh, C. Rowlands, T. Hartung, Toxicol. Sci. 2018, 165, 198–212.
A. Banerjee, K. Roy, Mol. Divers. 2022, 26, 2847–2862.
A. Banerjee, A. Gajewicz-Skretna, K. Roy, Mol. Inform. 2023, 42, 2200261.
S. Ghosh, M. Chatterjee, K. Roy, J. Agric. Food Chem. 2023, 71, 9538–9548.
S. Pore, A. Banerjee, K. Roy, Sustain. Energy Fuels 2023, 7, 3412–3431.
V. Vishwesh, R. Rajesh, S. P. Oikonomopoulos, B. K. Alsberg, J. Cheminformatics 2018, 10, 1758–2946.
A. Mauri, alvaDesc: A tool to calculate and Analyze Molecular Descriptors and Fingerprints (Ed.: K. Roy), Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, Humana, New York, 2020.
S. Wold, K. Esbensen, P. Geladi, Chemom. Intell. Lab. Syst. 1987, 2, 37–52.
P. Gramatica, Comput. Toxicol. 2013, 2, 499–526.
M. Goodarzi, B. Dejaegher, Y. V. Heyden, J. AOAC Int. 2012, 95, 636–651.
M. Shahlaei, Chem. Rev. 2013, 113, 8093–8103.
J. Cai, J. Luo, S. Wang, S. Yang, Neurocomputing 2018, 300, 70–79.
J. Devillers, Genetic Algorithms in Molecular Modeling, Academic Press, Lyon, France, 1996.
S. Wold, M. Sjöström, L. Eriksson, Chemom. Intell. Lab. Syst. 2001, 58, 109–130.
R. J. Chase, D. R. Harrison, A. Burke, G. M. Lackmann, A. McGovern, Weather Forecast 2022, 37, 1509–1529.
Z. Wu, D. Li, J. Meng, H. Wang, Introduction to SIMCA−P and Its Application (Ed.: V. Esposito Vinzi, W. Chin, J. Henseler, H. Wang), Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg, 2010.
S. M. Lundberg, S. I. Lee, Adv. Neural Inf. Process. Syst. 2017, 30, 4765–4774.
L. Shapley, in 7. A Value for n-Person Games. Contributions to the Theory of Games II (1953) 307–317, Classics in Game Theory (Ed.: H. W. Kuhn), Princeton University Press, Princeton, 1997, pp. 69–79.
A. Holzinger, A. Saranti, C. Molnar, P. Biecek, W. Samek, Explainable AI Methods - A Brief Overview (Ed.: A. Holzinger, R. Goebel, R. Fong, T. Moon, K R. Müller, W. Samek), xxAI - Beyond Explainable AI. xxAI 2020. Lecture Notes in Computer Science, vol. 13200, Springer, Cham. 2022.
R. Rodríguez-Pérez, J. Bajorath, J. Comput. Aided Mol. Des. 2020, 34, 1013–1026.
A. Moncada-Torres, M. C. van Maaren, M. P. Hendriks, S. Siesling, G. Geleijnse, Sci. Rep. 2021, 11, 6968.
A. Banerjee, K. Roy, Environ. Sci.: Processes Impacts 2023, 25, 1626–1644.
A. Banerjee, S. Kar, S. Pore, K. Roy, Nanotoxicology 2023, 17, 78–93.
G. Sun, P. Bai, T. Fan, L. Zhao, R. Zhong, R. S. McElhinney, T. B. H. McMurry, D. J. Donnelly, J. E. McCormick, J. Kelly, G. P. Margison, Pharmaceutics 2023, 15, 2170.
A. Banerjee, K. Roy, Chem. Res. Toxicol. 2023, 36, 1518–1531.
Grant Information:
All India Council for Technical Education (AICTE); Life Science Research Board, DRDO, New Delhi
Contributed Indexing:
Keywords: dye-sensitized solar cell (DSSC); machine learning (ML); power conversion efficiency (PCE); q-RASPR; read-across; similarity
Substance Nomenclature:
0 (Coloring Agents)
Entry Date(s):
Date Created: 20240220 Date Completed: 20250508 Latest Revision: 20250508
Update Code:
20260130
DOI:
10.1002/minf.202300210
PMID:
38374528
Database:
MEDLINE
*Further Information*
*The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47.
(© 2024 Wiley‐VCH GmbH.)*