Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors.
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ABSTRACT:
SUBMITTER: Zhao ZW
PROVIDER: S-EPMC9189862 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Organic materials combining high electron affinity with strong absorption in the visible spectrum are of interest for photodetector applications. In this study, we report two such molecular semiconductors, based upon an acceptor-donor-acceptor (A-D-A) approach. Coupling of an acceptor end group, 2,1,3-benzothiadiazole-4,5,6-tricarbonitrile (TCNBT), with a donor cyclopentadithiophene core affords materials with a band gap of 1.5 eV and low-lying LUMO levels around -4.2 eV. Both materials were readily synthesized by a one-pot nucleophilic displacement of a fluorinated precursor by cyanide. The two acceptors only differ in the nature of the solubilizing alkyl chain, which is either branched 2-ethyl hexyl (EH-TCNBT) or linear octyl (O-TCNBT). Both acceptors were blended with polymer donor PTQ10 as an active layer in OPDs. Significant device differences were observed depending on the alkyl chain, with the branched acceptor giving the optimum performance. Both acceptors exhibited very low dark current densities, with values up to 10-5 mA cm-2 at -2 V, highlighting the potential of the highly cyanated cores (TCNBT) as acceptor materials.
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Project description:This work was inspired by a previous report [Janjua, M. R. S. A. Inorg. Chem. 2012, 51, 11306-11314] in which the optoelectronic properties were improved with an acceptor bearing heteroaromatic rings. Herein, we have designed four novel Y-series non-fullerene acceptors (NFAs) by end-capped acceptor modifications of a recently synthesized 15% efficient Y21 molecule for better optoelectronic properties and their potential use in solar cell applications. Density functional theory (DFT) along with time-dependent density functional theory (TDDFT) at the B3LYP/6-31G(d,p) level of theory is used to calculate the band gap, exciton binding energy along with transition density matrix (TDM) analysis, reorganizational energy of electrons and holes, and absorption maxima and open-circuit voltage of investigated molecules. In addition, the PM6:YA1 complex is also studied to understand the charge shifting from the donor polymer PM6 to the NFA blend. Results of all parameters suggest that the DA'D electron-deficient core and effective end-capped acceptors in YA1-YA4 molecules form a perfect combination for effective tuning of optoelectronic properties by lowering frontier molecular orbital (FMO) energy levels, reorganization energy, and binding energy and increasing the absorption maximum and open-circuit voltage values in selected molecules (YA1-YA4). The combination of extended conjugation and excellent electron-withdrawing capability of the end-capped acceptor moiety in YA1 makes YA1 an excellent organic solar cell (OSC) candidate owing to promising photovoltaic properties including the lowest energy gap (1.924 eV), smallest electron mobility (λe = 0.0073 eV) and hole mobility (λh = 0.0083 eV), highest λmax values (783.36 nm (in gas) and 715.20 nm (in chloroform) with lowest transition energy values (E x) of 1.58 and 1.73 eV, respectively), and fine open-circuit voltage (V oc = 1.17 V) with respect to HOMOPM6-LUMOacceptor. Moreover, selected molecules are observed to have better photovoltaic properties than Y21, thus paving the way for experimentalists to look for future developments of Y-series-based highly efficient solar cells.
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Project description:The proposition of non-fullerene acceptors (NFAs) in organic solar cells has made great progress in the raise of power conversion efficiency, and it also broadens the ways for searching and designing new acceptor molecules. In this work, the design of novel NFAs with required properties is performed with the conditional generative model constructed from a convolutional neural network (CNN). The temporal CNN is firstly trained to be a good string-based molecular conditional generative model to directly generate the desired molecules. The reliability of generated molecular properties is then demonstrated by a graph-based prediction model and evaluated with quantum chemical calculations. Specifically, the global attention mechanism is incorporated in the prediction model to pool the extracted information of molecular structures and provide interpretability. By combining the generative and prediction models, thousands of NFAs with required frontier molecular orbital energies are generated. The generated new molecules essentially explore the chemical space and enrich the database of transformation rules for molecular design. The conditional generation model can also be trained to generate the molecules from molecular fragments, and the contribution of molecular fragments to the properties is subsequently predicted by the prediction model.
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