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

Abstract

Although singlet fission presents deep advantages or the generation of solar energy, the list of efficient singlet fission sensitizers is still very short, encouraging the theoreticians to focus their efforts on selecting and designing new candidates. Here, it is presented a computational protocol for the efficient screening of databases to select those species matching the energy requirements for singlet fission. Hence, out of the initial 29,123 species, 254 molecules (0.87%) were found to match singlet fission energy conditions. The consideration of practical concerns such as availability or stability reduced the number to just 24 (0.08%), among which the aminoanthraquinone derivatives are found particularly promising. The proposed protocol correct the deficiencies of the preceding ones, reaching DFT accuracy and minimizing the risk of getting false negatives especially at the early stages of the screening. In addition, this protocol can be used in future high‐throughput investigations using datasets composed of millions of species.

Singlet Fission Driven by Excited‐State Intramolecular Proton Transfer (ESIPT+SF): A (TD)DFT Study

“…[30] However, although the E[C 1 ] found in the β form is significantly lower compared to that found in the α counterpart, it is still clearly positive and therefore unfit for SF. The aminoanthraquinones 2 and 3 feature the opposite situation, and both tautomers (α and β) show slightly positive energetics, [31] which are known to be adequate for SF (e. g. tetracene). However, ESIPT can not be achieved because E…”

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

“…However, the poor photostability of these systems and their limitations for practical implementations has urged the search for more diverse SF-active materials. 20 − 22 In this context, other families of SF materials such as carotenoids 23 and conjugated polymers 24 , 25 have been considered, although so far to a much less extent. The one-dimensional sequence of chromophores in extended polymers opens the possibility of iSF through their characteristic lowest singlet excited state of a multiexciton character.…”

“…Since then, extensive experimental − and theoretical − work on acene derivatives has significantly pushed the understanding of the SF process. However, the poor photostability of these systems and their limitations for practical implementations has urged the search for more diverse SF-active materials. − In this context, other families of SF materials such as carotenoids and conjugated polymers , have been considered, although so far to a much less extent. The one-dimensional sequence of chromophores in extended polymers opens the possibility of iSF through their characteristic lowest singlet excited state of a multiexciton character. − This feature, together with their high tunability and easy processability, makes conjugated polymers particularly attractive for the development of novel iSF materials.…”

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

“…In this work, DRC was calculated with the resource demanding CASSCF method. Meanwhile, the group of Corminboeuf demonstrated a new strategy for design of intermolecular SF materials and screened existing copolymer materials by using high-throughput TDA calculations of donor–acceptor dimers. , Recently, Lopez-Carballeira et al also applied TD-DFT based screening protocol for finding new SF materials in CCSD databases and reported that only 254 molecules (0.87%) match the feasibility conditions out of which only 24 (0.08%) are of practical concern …”

“…29,30 Recently, Lopez-Carballeira et al also applied TD-DFT based screening protocol for finding new SF materials in CCSD databases and reported that only 254 molecules (0.87%) match the feasibility conditions out of which only 24 (0.08%) are of practical concern. 31 Nowadays, we live in the data science era and new evidence is collected daily for the effectiveness of machine learning (ML) algorithms in the discovery of new advanced materials. 32−39 However, the scientific papers combining ML and SF are intermittent.…”