A new protocol for the identification of singlet fission sensitizers through computational screening

High throughput selection of organic cathode materials

Efficient and affordable batteries require the design of novel organic electrode materials to overcome the drawbacks of the traditionally used inorganic materials, and the computational screening of potential candidates is a very efficient way to identify prospective solutions and minimize experimental testing. Here we present a DFT high-throughput computational screening where 86 million molecules contained in the PUBCHEM database have been analyzed and classified according to their estimated electrochemical features. The 5445 top-performing candidates were identified, and among them, 2306 are expected to have a one-electron reduction potential higher than 4 V versus (Li/Li+ ). Analogously, one-electron energy densities higher than 800 Whkg-1 have been predicted for 626 molecules. Explicit calculations performed for certain materials show that at least 69 candidates with a two-electron energy density higher than 1300 Whkg-1 . Successful molecules were sorted into several families, some of them already commonly used electrode materials, and others still experimentally untested. Most of them are small systems containing conjugated CO, NN, or NC functional groups. Our selected molecules form a valuable starting point for experimentalists exploring new materials for organic electrodes.

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets

Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.

Optimizing the Thermodynamics and Kinetics of the Triplet-Pair Dissociation in Donor-Acceptor Copolymers for Intramolecular Singlet Fission

Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials, in particular, have attracted growing interest as they can be easily implemented in single-junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, whose mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. Overall, these results provide a better understanding of the intramolecular 1TT dissociation process and establish a new paradigm for the development of novel iSF active materials.