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

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Abstract

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.

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

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Publication details

The article was received on 12 Sep 2018, accepted on 30 Oct 2018 and first published on 30 Oct 2018


Article type: Communication
DOI: 10.1039/C8MH01135D
Citation: Mater. Horiz., 2019, Advance Article
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    Combining electronic and structural features in machine learning models to predict organic solar cells properties

    D. Padula, J. D. Simpson and A. Troisi, Mater. Horiz., 2019, Advance Article , DOI: 10.1039/C8MH01135D

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