Evaluation of Molecular Fingerprints for Determining Dye Aggregation on Semiconductor Surfaces.
aggregation
classification
dye sensitized solar cells
machine learning
molecular fingerprints
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
received:
03
04
2020
accepted:
31
05
2020
pubmed:
2
6
2020
medline:
3
5
2022
entrez:
2
6
2020
Statut:
ppublish
Résumé
Dye aggregation plays an important role in determining the photovoltaic performance of dye sensitized solar cells. Compared with the spectra observed in solution, it is, apriori, difficult to ascertain whether a dye is likely to show hypsochromic (H) or bathochromic (J) aggregation, until after adsorption onto the semiconductor electrode. Herein, we show that molecular fingerprint-based methods provide a fast and efficient way to discriminate between H- and J-aggregating dyes. The efficacy of the fingerprint-based classification models is demonstrated with a diverse set of over 3000 organic dyes dissolved in different solvents. Requiring only the structure of the dye and the polarity of the solvent used, the machine learning model achieves close to 80 % classification accuracies that are comparable with models based on a combination of fragment counts and topological indices. For interested researchers, we have bundled the prediction tools as an R package.
Identifiants
pubmed: 32476288
doi: 10.1002/minf.202000062
doi:
Substances chimiques
Coloring Agents
0
Solvents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000062Informations de copyright
© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
A. Hagfeldt, G. Boschloo, L. Sun, L. Kloo, H. Pettersson, Chem. Rev. 2010, 110, 6595-6663;
A. Carella, F. Borbone, R. Centore, Front. Chem. 2018, 6, 481.
Z.-S. Huang, H. Meier, D. Cao, J. Mater. Chem. C 2016, 4, 2404-2426;
A. Mahmood, Sol. Energy 2016, 123, 127-144.
V. Venkatraman, R. Raju, S. P. Oikonomopoulos, B. K. Alsberg, J. Cheminf. 2018, 10, 18.
L. Zhang, J. M. Cole, J. Mater. Chem. A 2017, 5, 19541-19559.
C. Anselmi, E. Mosconi, M. Pastore, E. Ronca, F. De Angelis, Phys. Chem. Chem. Phys. 2012, 14, 15963-15974.
F. Ambrosio, N. Martsinovich, A. Troisi, J. Phys. Chem. Lett. 2012, 3, 1531-1535;
N. Martsinovich, D. R. Jones, A. Troisi, J. Phys. Chem. C 2010, 114, 22659-22670;
M. Pastore, F. De Angelis, ACS Nano 2010, 4, 556-562;
P. Prajongtat, S. Suramitr, S. Nokbin, K. Nakajima, K. Mitsuke, S. Hannongbua, J. Mol. Graphics Modell. 2017, 76, 551-561;
L. Zhang, K. Xu, Appl. Surf. Sci. 2020, 502, 144139.
R. Sánchez-de-Armas, M. Á. San Miguel, J. Oviedo, J. F. Sanz, Phys. Chem. Chem. Phys. 2012, 14, 225-233;
E. Castellucci, M. Monini, M. Bessi, A. Iagatti, L. Bussotti, A. Sinicropi, M. Calamante, L. Zani, R. Basosi, G. Reginato, A. Mordini, P. Foggi, M. Di Donato, Phys. Chem. Chem. Phys. 2017, 19, 15310-15323.
L. Zhang, X. Liu, W. Rao, J. Li, Sci. Rep. 2016, 6, 35893;
L. Zhang, J. M. Cole, ACS Appl. Mater. Interfaces 2014, 6, 15760-15766.
A. Paul, A. Furmanchuk, W.-K. Liao, A. Choudhary, A. Agrawal, Mol. Inf. 2019, 38, 1900038.
V. Venkatraman, B. K. Alsberg, Polymer 2018, 10, 103.
V. Venkatraman, S. Evjen, K. C. Lethesh, J. J. Raj, H. K. Knuutila, A. Fiksdahl, Sustain. Energy Fuels 2019, 3, 2798-2808;
V. Venkatraman, M. Gupta, M. Foscato, H. F. Svendsen, V. R. Jensen, B. K. Alsberg, Int. J. Greenhouse Gas Control 2016, 49, 55-63.
F. Li, X. Peng, Z. Wang, Y. Zhou, Y. Wu, M. Jiang, M. Xu, Energy Environ. Mater. 2019, 2, 280-291.
M. Yang, B. Tao, C. Chen, W. Jia, S. Sun, T. Zhang, X. Wang, J. Chem. Inf. Model. 2019, 59, 5002-5012;
Q. Zang, K. Mansouri, A. J. Williams, R. S. Judson, D. G. Allen, W. M. Casey, N. C. Kleinstreuer, J. Chem. Inf. Model. 2017, 57, 36-49;
S. Zheng, M. Jiang, C. Zhao, R. Zhu, Z. Hu, Y. Xu, F. Lin, Front. Chem. 2018, 6.
D. C. Elton, Z. Boukouvalas, M. S. Butrico, M. D. Fuge, P. W. Chung, Sci. Rep. 2018, 8, 9059.
V. Venkatraman, A. E. Yemene, J. de Mello, Sci. Rep. 2019, 9, 16983.
E. L. Willighagen, J. W. Mayfield, J. Alvarsson, A. Berg, L. Carlsson, N. Jeliazkova, S. Kuhn, T. Pluskal, M. Rojas-Chertó, O. Spjuth, G. Torrance, C. T. Evelo, R. Guha, C. Steinbeck, J. Cheminf. 2017, 9, 33.
L. R. Snyder, J. Chromatogr. A 1974, 92, 223-230.
Y. Ooyama, Y. Oda, T. Mizumo, Y. Harima, J. Ohshita, Eur. J. Org. Chem. 2013, 2013, 4533-4538.
A. Cereto-Massagué, M. J. Ojeda, C. Valls, M. Mulero, S. Garcia-Vallvé, G. Pujadas, Methods 2015, 71, 58-63.
M. Sud, J. Chem. Inf. Model. 2016, 56, 2292-2297;
R. Guha, J Stat. Soft. 2007, 18, 16.
ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.txt.
J. L. Durant, B. A. Leland, D. R. Henry, J. G. Nourse, J. Chem. Inf. Model. 2002, 42, 1273-1280.
D. Rogers, M. Hahn, J. Chem. Inf. Model. 2010, 50, 742-754.
L. H. Hall, L. B. Kier, J. Chem. Inf. Model. 1995, 35, 1039-1045.
R Core Team, Foundation for Statistical Computing, Vienna, Austria, 2019.
K. L. Gwet, Psychometrika 2008, 73, 407.
K. H. Brodersen, C. S. Ong, K. E. Stephan, J. M. Buhmann, in 2010 20th International Conference on Pattern Recognition, 2010, pp. 3121-3124.
L. van der Maaten, G. Hinton, J Mach. Learn. Res. 2008, 9, 2579-2605.
M. L. McHugh, Biochem. Med. 2012, 22, 276-282.
I. Nouretdinov, S. G. Costafreda, A. Gammerman, A. Chervonenkis, V. Vovk, V. Vapnik, C. H. Y. Fu, NeuroImage 2011, 56, 809-813;
U. Norinder, L. Carlsson, S. Boyer, M. Eklund, J. Chem. Inf. Model. 2014, 54, 1596-1603.
S. Zhang, H. Tong, J. Xu, R. Maciejewski, Comput. Soc. Netw. 2019, 6, 11.
T. Meng, X. Jing, Z. Yan, W. Pedrycz, Inform. Fusion 2020, 57, 115-129.
B. Bienfait, P. Ertl, J. Cheminf. 2013, 5, 24.
V. Venkatraman, K. C. Lethesh, Data 2020, 5.