Theoretical studies for forecasting the power conversion efficiencies of polymer-based organic photovoltaic cells

Photophysical properties of donor (D)-acceptor (A)-donor (D) diketopyrrolopyrrole (A) systems as donors for applications to organic electronic devices

Fourteen substituted diketopyrrolopyrrole (DPP) molecules in a donor (D)-acceptor (DPP)-donor (D) arrangement were designed. We employed density functional theory, time-dependent DFT, DFT-MRCI and the ab initio wave function second-order algebraic diagrammatic construction (ADC(2)) methods to investigate theoretically these systems. The examined aromatic substituents have one, two, or three hetero- and non-hetero rings. We comprehensively investigated their optical, electronic, and charge transport properties to evaluate potential applications in organic electronic devices. We found that the donor substituents based on one, two, or three aromatic rings bonded to the DPP core can improve the efficiency of an organic solar cell by fine-tuning the highest occupied molecular orbital/lowest unoccupied molecular orbital levels to match acceptors in typical bulk heterojunctions acceptors. Several properties of interest for organic photovoltaic devices were computed. We show that the investigated molecules are promising for applications as donor materials when combined with typical acceptors in bulk heterojunctions because they have appreciable energy conversion efficiencies resulting from their low ionization potentials and high electron affinities. This scenario allows a more effective charge separation and reduces the recombination rates. A comprehensive charge transfer analysis shows that D-A (DDP)-D systems have significant intramolecular charge transfer, further confirming their promise as candidates for donor materials in solar cells. The significant photophysical properties of DPP derivatives, including the high fluorescence emission, also allow these materials to be used in organic light-emitting diodes.

A comprehensive analysis of charge transfer effects on donor-pyrene (bridge)-acceptor systems using different substituents

The alternant polycyclic aromatic hydrocarbon pyrene has photophysical properties that can be tuned with different donor and acceptor substituents. Recently, a D (donor)-Pyrene (bridge)-A (acceptor) system, DPA, with the electron donor N,N-dimethylaniline (DMA), and the electron acceptor trifluoromethylphenyl (TFM), was investigated by means of time-resolved spectroscopic measurements (J. Phys. Chem. Lett. 2021, 12, 2226-2231). DPA shows great promise for potential applications in organic electronic devices. In this work, we used the ab initio second-order algebraic diagrammatic construction method ADC(2) to investigate the excited-state properties of a series of analogous DPA systems, including the originally synthesized DPAs. The additionally investigated substituents were amino, fluorine, and methoxy as donors and nitrile and nitro groups as acceptors. The focus of this work was on characterizing the lowest excited singlet states regarding charge transfer (CT) and local excitation (LE) characters. For the DMA-pyrene-TFM system, the ADC(2) calculations show two initial electronic states relevant for interpreting the photodynamics. The bright S1 state is locally excited within the pyrene moiety, and an S2 state is localized ~0.5 eV above S1 and characterized as a donor to pyrene CT state. HOMO and LUMO energies were employed to assess the efficiency of the DPA compounds for organic photovoltaics (OPVs). HOMO-LUMO and optical gaps were used to estimate power conversion and light-harvesting efficiencies for practical applications in organic solar cells. Considering the systems using smaller D/A substituents, compounds with the strong acceptor NO2 substituent group show enhanced CT and promising properties for use in OPVs. Some of the other compounds with small substituents are also found to be competitive in this regard.

A Hammett's analysis of the substituent effect in functionalized diketopyrrolopyrrole (DPP) systems: Optoelectronic properties and intramolecular charge transfer effects

Diketopyrrolopyrrole (DPP) systems have promising applications in different organic electronic devices. In this work, we investigated the effect of 20 different substituent groups on the optoelectronic properties of DPP-based derivatives as the donor ( D )-material in an organic photovoltaic (OPV) device. For this purpose, we employed Hammett's theory (HT), which quantifies the electron-donating or -withdrawing properties of a given substituent group. Machine learning (ML)-basedσ m ,σ p ,σ m 0 ,σ p 0 ,σ p + ,σ p - ,σ I , andσ R Hammett's constants previously determined were used. Mono- (DPP-X1 ) and di-functionalized (DPP-X2 ) DPPs, where X is a substituent group, were investigated using density functional theory (DFT), time-dependent DFT (TDDFT), and ab initio methods. Several properties were computed using CAM-B3LYP and the second-order algebraic diagrammatic construction, ADC(2), an ab initio wave function method, including the adiabatic ionization potential ( I P A ), the electron affinity ( E A A ), the HOMO-LUMO gaps (E g ), and the maximum absorption wavelengths (λ max ), the first excited state transition 1 S0 → 1 S1 energies ( ∆ E ) (the optical gap), and exciton binding energies. From the optoelectronic properties and employing typical acceptor systems, the power conversion efficiency ( PCE ), open-circuit voltage (V OC ), and fill factor ( FF ) were predicted for a DPP-based OPV device. These photovoltaic properties were also correlated with the machine learning (ML)-based Hammett's constants. Overall, good correlations between all properties and the different types of σ constants were obtained, except for theσ I constants, which are related to inductive effects. This scenario suggests that resonance is the main factor controlling electron donation and withdrawal effects. We found that substituent groups with large σ values can produce higher photovoltaic efficiencies. It was also found that electron-withdrawing groups (EWGs) reducedE g and ∆ E considerably compared to the unsubstituted DPP-H. Moreover, for every decrease (increase) in the values of a given optoelectronic property of DPP-X1 systems, a more significant decrease (increase) in the same values was observed for the DPP-X2 , thus showing that the addition of the second substituent results in a more extensive influence on all electronic properties. For the exciton binding energies, an unsupervised machine learning algorithm identified groups of substituents characterized by average values (centroids) of Hammett's constants that can drive the search for new DDP-derived materials. Our work presents a promising approach by applying HT on molecular engineering DPP-based molecules and other conjugated molecules for applications on organic optoelectronic devices.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

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

We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.

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

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.