Combining electronic and structural features in machine learning models to predict organic solar cells properties

We present a translation of the chemical intuition in materials discovery, in terms of chemical similarity of efficient materials, into a rigorous framework exploiting machine learning. We computed equilibrium geometries and electronic properties (DFT) for a database of 249 Organic donor–acceptor pa...

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Bibliographic Details
Published inMaterials horizons Vol. 6; no. 2; pp. 343 - 349
Main Authors Padula, Daniele, Simpson, Jack D., Troisi, Alessandro
Format Journal Article
LanguageEnglish
Published Cambridge Royal Society of Chemistry 01.02.2019
Subjects
Artificial intelligence
Chemical fingerprinting
Combinatorial analysis
Donors (electronic)
Machine learning
Materials selection
Mathematical models
Organic chemistry
Parameters
Photovoltaic cells
Predictions
Properties (attributes)
Quantum chemistry
Regression analysis
Similarity
Solar cells
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Summary:We present a translation of the chemical intuition in materials discovery, in terms of chemical similarity of efficient materials, into a rigorous framework exploiting machine learning. We computed equilibrium geometries and electronic properties (DFT) for a database of 249 Organic donor–acceptor pairs. We obtain similarity metrics between pairs of donors in terms of electronic and structural parameters, and we use such metrics to predict photovoltaic efficiency through linear and non-linear machine learning models. We observe that using only electronic or structural parameters leads to similar results, while considering both parameters at the same time improves the predictive capability of the models up to correlations of r ≈ 0.7. Such correlation allows for reliable predictions of efficient materials, and lends to be coupled with combinatorial of evolutionary approaches for a more reliable virtual screening of candidate materials.
ISSN:2051-6347
2051-6355
DOI:10.1039/C8MH01135D