Virtual Screening of Hole Transport, Electron Transport, and Host Layers for Effective OLED Design

Efficient Large-Area (81 cm2) Ternary Copper Halides Light-Emitting Diodes with External Quantum Efficiency Exceeding 13% via Host-Guest Strategy

Ternary copper (Cu) halides are promising candidates for replacing toxic lead halides in the field of perovskite light-emitting diodes (LEDs) toward practical applications. However, the electroluminescent performance of Cu halide-based LEDs remains a great challenge due to the presence of serious nonradiative recombination and inefficient charge transport in Cu halide emitters. Here, the rational design of host-guest [dppb]2Cu2I2 (dppb denotes 1,2-bis[diphenylphosphino]benzene) emitters and its utility in fabricating efficient Cu halide-based green LEDs that show a high external quantum efficiency (EQE) of 13.39% are reported. The host-guest [dppb]2Cu2I2 emitters with mCP (1,3-bis(N-carbazolyl)benzene) host demonstrate a significant improvement of carrier radiative recombination efficiency, with the photoluminescence quantum yield increased by nearly ten times, which is rooted in the efficient energy transfer and type-I energy level alignment between [dppb]2Cu2I2 and mCP. Moreover, the charge-transporting mCP host can raise the carrier mobility of [dppb]2Cu2I2 films, thereby enhancing the charge transport and recombination. More importantly, this strategy enables a large-area prototype LED with a record-breaking area up to 81 cm2, along with a decent EQE of 10.02% and uniform luminance. It is believed these results represent an encouraging stepping stone to bring Cu halide-based LEDs from the laboratory toward commercial lighting and display panels.

Precise Regulation on the Bond Dissociation Energy of Exocyclic C-N Bonds in Various N-Heterocycle Electron Donors via Machine Learning

Heterocycles with saturated N atoms (HetSNs) are widely used electron donors in organic light-emitting diode (OLED) materials. Their relatively low bond dissociation energy (BDE) of exocyclic C-N bonds has been closely related to material intrinsic stability and even device lifetime. Thus, it is imperative to realize fast prediction and precise regulation of those C-N BDEs, which demands a deep understanding of the relationship between the molecular structure and BDE. Herein, via machine learning (ML), we rapidly and accurately predicted C-N BDEs in various HetSNs and found that five-membered HetSNs (5-HetSNs) have much higher BDEs than almost all 6-HetSNs, except emerging boron-N blocks. Thorough analysis disclosed that high aromaticity is the foremost factor accounting for the high BDE of 5-HetSNs, and introducing intramolecular hydrogen-bond or electron-withdrawing moieties could also increase BDE. Importantly, the ML models performed well in various realistic OLED materials, showing great potential in characterizing material intrinsic stability for high-throughput virtual-screening and material design efforts.

Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods

Genetic algorithm (GA) optimization coupled with the semiempirical intermediate neglect of differential overlap (INDO)/CIS method is presented to inversely design the red thermally activation delayed fluorescent (TADF) molecules. According to the predefined donor-acceptor (DA) library to build an AD_n-type TADF candidate, we utilized the chemical notation language SMILES code to generate a TADF molecule and apply the RDKit program to produce the initial 3D molecular structure. A combined fitness function is proposed to evaluate the performance of the functional-lead TADF molecule. The fitness function includes three key parameters, i.e., the emission wavelength, the energy gap (ΔE_ST) between the lowest singlet (S₁)- and triplet (T₁)-excited states, and the oscillator strengths for electron transition from S₀ and S₁. A cheap QM method, i.e., INDO/CIS, on the basis of an xTB-optimized molecular geometry is applied to quickly calculate the fitness function. Finally, the GA approach is utilized to globally search for the wavelength-specific TADF molecules under our predefined DA library, and the optimum 630 nm red and 660 nm deep red TADF molecules are inversely designed according to the evolution of molecular fitness functions.

Theoretical analysis of OLED performances of some aromatic nitrogen-containing ligands

It is well-known that tris(8-hydroxyquinoline) aluminum (Alq3) complex and N,N'diphenyl-N,N'-bis(3-methylphenyl)-1,1'-diphenyl-4,4'-diamine compound (TPD) are widely used as electron transfer material (ETL) and hole transfer material (HTL) in organic light emitting diode (OLED) structure, respectively. Considering the reference materials, in the present work, the OLED performances of some cyclic aromatic structures such as 4,4'azopyridine [AZPY], 4,4'-bipyridine [BIPY], 1,2-bis[4'-(4-methylphenyl)2,2':6'2″-terpyridin6-yl]ethyne (BISTERPY), 5,5'-diamino-2,2'-bipyridine (DABP), dipyrido[3,2-a:2',3'c]phenazine (DPP), 4,7-phenanthroline (PHEN) including nitrogen atom have been theoretically analyzed. It is important to note that B3LYP/6-31G(d) and B3LYP/TZP levels of the theory were taken into account for the calculations about monomeric and dimeric structures, respectively. Additionally, the calculations of the mentioned monomeric form were performed at B3LYP-D3/6-31G, CAM-B3LYP/6-31G and ωB97X-D/6-31G(d) levels. For a detailed theoretical analysis, the reorganization energies (λe and λh), adiabatic and vertical ionization potentials and electron affinities, the effective transfer integrals (Ve and Vh), and the charge transfer rates (We and Wh) of all compounds were computed by means of computational chemistry tools. In the light of calculated parameters, it is determined that these mentioned aromatic cyclic structures will be used in which layers of OLED structure. The results obtained in this study will be helpful in the design and applications of new molecules as OLED materials in the future.

Organic Photoredox Catalysts for CO2 reduction: Driving Discovery with Genetic Algorithms

This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO2 reduction that are both highly active and resistant to degradation. The LUMO energy of the ground state catalyst is chosen as the activity descriptor and average Mulliken charge on all ring carbons as the descriptor for resistance to degradation via carboxylation (both obtained using density functional theory), to construct the fitness function of the GA. We combine the results of multiple GA runs, each based on different relative weighting of the two descriptors, and rigorously assess GA performance by calculating electron transfer barriers to CO2 reduction. A large majority of GA predictions exhibit improved performance relative to experimentally studied o-, m-, and p-terphenyl catalysts. Based on stringent cut-offs imposed on the average charge, barrier to electron transfer to CO2, and excitation energy, we recommend 25 catalysts for further experimental investigation of viability towards photoredox CO2 reduction.

Photoredox Chemistry with Organic Catalysts: Role of Computational Methods

Organic catalysts have the potential to carry out a wide range of otherwise thermally inaccessible reactions via photoredox routes. Early demonstrated successes of organic photoredox catalysts include one-electron CO₂ reduction and H₂ generation via water splitting. Photoredox systems are challenging to study and design owing to the sheer number and diversity of phenomena involved, including light absorption, emission, intersystem crossing, partial or complete charge transfer, and bond breaking or formation. Designing a viable photoredox route therefore requires consideration of a host of factors such as absorption wavelength, solvent, choice of electron donor or acceptor, and so on. Quantum chemistry methods can play a critical role in demystifying photoredox phenomena. Using one-electron CO₂ reduction with phenylene-based chromophores as an illustrative example, this perspective highlights recent developments in quantum chemistry that can advance our understanding of photoredox processes and proposes a way forward for driving the design and discovery of organic catalysts.

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here, we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for the excited-state energy of oligothiophenes against time-dependent density functional theory (TDDFT) calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiophene) in its single-crystal and solution phases using the transfer learning models trained with the data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.

Free Marine Natural Products Databases for Biotechnology and Bioengineering

Marine organisms and micro-organisms are a source of natural compounds with unique chemical features. These chemical properties are useful for the discovery of new functions and applications of marine natural products (MNPs). To extensively exploit the potential implementations of MNPs, they are gathered in chemical databases that allow their study and screening for applications of biotechnological interest. However, the classification of MNPs is currently poor in generic chemical databases. The present availability of free-access-focused MNP databases is scarce and the molecular diversity of these databases is still very low when compared to the paid-access ones. In this review paper, the current scenario of free-access MNP databases is presented as well as the hindrances involved in their development, mainly compound dereplication. Examples and opportunities for using freely accessible MNP databases in several important areas of biotechnology are also assessed. The scope of this paper is, as well, to notify the latent potential of these information sources for the discovery and development of new MNPs in biotechnology, and push future efforts to develop a public domain MNP database freely available for the scientific community.