Computational Identification of Novel Families of Nonfullerene Acceptors by Modification of Known Compounds

Abstract

We considered a database of tens of thousands of known organic semiconductors and identified those compounds with computed electronic properties (orbital energies, excited state energies, and oscillator strengths) that would make them suitable as nonfullerene electron acceptors in organic solar cells. The range of parameters for the desirable acceptors is determined from a set of experimentally characterized high-efficiency nonfullerene acceptors. This search leads to ∼30 lead compounds never considered before for organic photovoltaic applications. We then proceed to modify these compounds to bring their computed solubility in line with that of the best small-molecule nonfullerene acceptors. A further refinement of the search can be based on additional properties like the reorganization energy for chemical reduction. This simple strategy, which relies on a few easily computable parameters and can be expanded to a larger set of molecules, enables the identification of completely new chemical families to be explored experimentally.

Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

“…Through manual consideration, four new polymers with the identical backbone and different alkyl chains were synthesized, one of which showed a PCE of 10.10% with IT-4F NFA, which is in good agreement with ML predictions of PCE (11.15%) and the choice of alkyl chains. In addition to the material design for NFA-OPV, − phase separation analysis and green solvent selection are other efficient approaches using ML.…”

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

“…A similar method was used to validate the screening of molecular semiconductors for SF, 37 TADF 367 and electron acceptors for OPV. 295 In all these cases less than 0.5% of the screened materials had the desirable property but a fraction of them were already known to possess this property providing a good statistical validation of the procedure. There is no consensus among practitioners in the field on whether it is best to pursue experimental validation of HTVS within the same research report.…”

“…The virtual screening in these cases often focuses on easily computable properties that capture only some of the physics. Examples are the search for alternative electron acceptor molecules for OPV that have similar electronic and solubility characteristics to the best performing ones, 295 or the development of statistical models only considering energy levels of the constituent molecules. 34 …”

Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase Via Machine Learning

“…On the microscopic level, the morphology is a direct manifestation of the molecular packing arrangement throughout the bulk of the material. Therefore, establishing a quantitative structure–property relationship (QSPR) would provide insights that guide the exploration of the high-dimensional molecular structural space more efficiently and is effective in accelerating the discovery of high-performance OSC materials. ,,, …”