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A: Molecular Structure, Quantum Chemistry, and General Theory
Machine Learning Energy Gaps of Porphyrins with Molecular Graph Representations Zheng Li, Noushin Omidvar, Wei Shan Chin, Esther Robb, Amanda J Morris, Luke E. K. Achenie, and Hongliang Xin J. Phys. Chem. A, Just Accepted Manuscript • DOI: 10.1021/acs.jpca.8b02842 • Publication Date (Web): 24 Apr 2018 Downloaded from http://pubs.acs.org on April 24, 2018
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The Journal of Physical Chemistry
Machine Learning Energy Gaps of Porphyrins with Molecular Graph Representations Zheng Li,† Noushin Omidvar,† Wei Shan Chin,† Esther Robb,† Amanda Morris,‡ Luke Achenie,† and Hongliang Xin∗,† †Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 ‡Department of Chemistry, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061 E-mail:
[email protected] 1 ACS Paragon Plus Environment
The Journal of Physical Chemistry
Abstract
Molecular functionalization of porphyrins opens countless new opportunities in tailoring their physicochemical properties for light-harvesting applications. However, the immense materials space spanned by a vast number of substituent ligands and chelating metal ions prohibits high-throughput screening of combinatorial libraries. In this work, machine learning algorithms equipped with the domain knowledge of chemical graph theory were employed for predicting the energy gaps of >12000 porphyrins from the Computational Materials Repository. Among a variety of graph-based molecular descriptors, the electrotopological-state index, which encodes electronic and topological structure information, captures the energy gaps of porphyrins with a prediction RMSE 12,000 molecular structures of porphyrins and DFT-calculated properties, e.g., frontier orbital energy levels, optical gaps, and energy gaps. In this study, we focus on the energy gap calculated as the difference between the electron affinity and ionization potential, i.e., Eea − Eip , because of its importance in determining efficiencies of solar light absorption and energy transfer. 11–19 By varying the side groups R1 , R2 , R3 , and the anchor group R4 at meso-positions, peripheral substituents L at β -positions, and chelating metal ions M as denoted in Fig. 1, a theoretically unlimited number of porphyrins can be conceived and synthesized. In the dataset, the side groups are aromatic ligands and the anchor group is connected to the methine bridge via a carbon-carbon double or triple bond and a carboxylate group as the anchor point to semiconducting supports, e.g, TiO2 . 11,12 For the porphyrins in the database, R1 and R3 ligands are kept the same, while R4 has two rotational configurations with respect to the 3 ACS Paragon Plus Environment
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Side Group R1
L
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L
L
L
Side Group R2
Metal Center M
Anchor Group R4
L
L L Side Group R3
L
Figure 1: Structural labeling scheme of porphyrins with varying ligand substitution and metal chelation.
porphyrin plane. A complete list of functional groups used in the dataset can be found in the Computational Materials Repository. 55 To have an overview of the distribution of porphyrins and their properties across the dataset, violin plots in Fig. S1 show that the metal chelating has little influence on the energy gaps of porphyrins (
