Toward Design of Novel Materials for Organic Electronics

Highly Transmissive, Processable, Highly Conducting and Stable Polythiophene Derivatives via Direct (Hetero)arylation Polymerization

The development of modern optoelectronic devices increases the need for lightweight and flexible transparent conductors. It is thus essential to develop new eco-friendly materials that can be easily processed for the fabrication of such devices. For this purpose, the synthesis of self-doped, highly conducting, transmissive, and water-processable polythiophene derivatives was performed via the direct heteroarylation polymerization method and a protection/deprotection strategy. Stable conductivities up to 1000 S cm-1 have been obtained. Champion materials (P1 and P7) were scaled and processed via the roll-to-roll compatible slot-die coating method to demonstrate their large area applicability. The best coated films exhibited optical transmittance greater than 79% at 550 nm with sheet resistances of 116 Ω □-1. These values are comparable to indium-tin oxide on plastic and thus present a viable alternative to metal oxide-based electrodes.

Electron Transfer Enhanced by a Minimal Energetic Driving Force at the Organic-Semiconductor Interface

The energetic driving force for electron transfer must be minimized to realize efficient optoelectronic devices including organic light-emitting diodes (OLEDs) and organic photovoltaics (OPVs). Exploring the dynamics of a charge-transfer (CT) state at an interface leads to a comprehension of the relationship between energetics, electron-transfer efficiency, and device performance. Here, we investigate the electron transfer from the CT state to the triplet excited state (T1) in upconversion OLEDs with 45 material combinations. By analyzing the CT emission and the singlet excited-state emission from triplet-triplet annihilation via the dark T1, their energetics and electron-transfer efficiencies are extracted. We demonstrate that the CT→T1 electron transfer is enhanced by the stronger CT interaction and a minimal energetic driving force (<0.1 eV), which is explained using the Marcus theory with a small reorganization energy of <0.1 eV. Through our analysis, a novel donor-acceptor combination for the OLED is developed and shows an efficient blue emission with an extremely low turn-on voltage of 1.57 V. This work provides a solution to control interfacial CT states for efficient optoelectronic devices without energy loss.

Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these relationships require the complex integration of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, these methods pose substantial computational challenges that limit their application to the requisite length scales of soft material morphologies. Here, we demonstrate the bottom-up electronic coarse-graining (ECG) of morphology-dependent electronic structure in the liquid-crystal-forming semiconductor, 2-(4-methoxyphenyl)-7-octyl-benzothienobenzothiophene (BTBT). ECG is applied to construct density functional theory (DFT)-accurate valence band Hamiltonians of the isotropic and smectic liquid crystal (LC) phases using only the CG representation of BTBT. By bypassing the atomistic resolution and its prohibitive computational costs, ECG enables the first calculations of the morphology dependence of the electronic structure of charge carriers across LC phases at the ∼20 nm length scale, with robust statistical sampling. Kinetic Monte Carlo (kMC) simulations reveal a strong morphology dependence on zero-field charge mobility among different LC phases as well as the presence of two-molecule charge carriers that act as traps and hinder charge transport. We leverage these results to further evaluate the feasibility of developing mesoscopic, field-based ECG models in future works. The fully CG approach to electronic property predictions in LC semiconductors opens a new computational direction for designing electronic processes in soft materials at their characteristic length scales.

Impact of Temperature on the Chiroptical Properties of Thin Films of Chiral Thiophene-based Oligomers

According to the theoretical model based on the Mueller matrix approach, the experimental electronic circular dichroism (ECD) for thin films of chiral organic dyes can be expressed as the sum of several contributions, two of which are the most significant: 1) an intrinsic component (CDiso) invariant upon sample orientation, reflecting the molecular and/or supramolecular chirality, due to 3D-chiral nanoscopic structures; 2) a non-reciprocal component (LDLB) which inverts its sign upon sample flipping, which arises from the interaction of linear dichroism and linear birefringence in locally anisotropic domains, expression of 2D-chiral micro/mesoscopic structures. In this work, we followed in parallel through ECD and differential scanning calorimetry (DSC) the temperature evolution of the supramolecular arrangements of thin films of five structurally related chiral thiophene-based oligomers with different LDLB/CDiso ratio. By increasing the temperature, regardless of phase transitions observed by DSC analysis, systems with strong CDiso revealed no changes in the ECD spectrum, while compounds with dominant LDLB contribution underwent a gradual (and reversible) reduction of (apparent) ECD signals. These findings demonstrated that the concomitant occurrence of intrinsic and non-reciprocal components in the ECD spectrum of thin films of chiral organic dyes is strictly correlated with solid-state organizations of different stability.

A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices

Materials informatics in the development of organic light-emitting diode (OLED) related materials have been performed and exhibited the effectiveness for finding promising compounds with a desired property. However, the molecular structure optimization of the promising compounds through the conventional approach, namely the fine-tuning of molecules, still involves a significant amount of trial and error. This is because it is challenging to endow a single molecule with all the properties required for practical applications. The present work focused on fine-tuning triazine-based electron-transport materials using machine learning (ML) techniques. The prediction models based on localized datasets containing only triazine derivatives showed high prediction accuracy. The descriptors from density functional theory calculations enhanced the prediction of the glass transition temperature. The proposed multistep virtual screening approach extracted the promising triazine derivatives with the coexistence of higher electron mobility and glass transition temperature. Nine selected triazine compounds from 3,670,000 of the initial search space were synthesized and used as the electron transport layer for practical OLED devices. Their observed properties matched the predicted properties, and they enhanced the current efficiency and lifetime of the device. This paper provides a successful model for the ML assisted fine-tuning that effectively accelerates the development of practical materials.

Synthesis, Photophysical Properties and Self-Assembly of a Tetraphenylethylene-Naphthalene Diimide Donor-Acceptor Molecule

The development of new π-conjugated molecular structures with controlled self-assembly and distinct photophysical properties is crucial for advancing applications in optoelectronics and biomaterials. This study introduces the synthesis and detailed self-assembly analysis of tetraphenylethylene (TPE) functionalized naphthalene diimide (NDI), a novel donor-acceptor molecular structure referred to as TPE-NDI. The investigation specifically focuses on elucidating the self-assembly behavior of TPE-NDI in mixed solvents of varying polarities, namely chloroform: methylcyclohexane (CHCl3 : MCH) and chloroform: methanol (CHCl3 : MeOH). Employing a several analytical methodologies, including UV-Vis absorption and fluorescence emission spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and dynamic light scattering (DLS), these self-assembled systems have been comprehensively examined. The results reveal that TPE-NDI manifests as distinct particles in CHCl3 : MCH (fMCH =90 %), while transitioning to flower-like assemblies in CHCl3 : MeOH (fMeOH =90 %). This finding underscores the critical role of solvent polarity in dictating the morphological characteristics of TPE-NDI self-assembled aggregates. Furthermore, the study proposes a molecular packing mechanism, based on SEM data, offering significant insights into the design and development of functional supramolecular systems. Such advancements in understanding the molecular self-assembly new π-conjugated molecular structures are anticipated to pave the way for novel applications in material science and nanotechnology.

Chiral diketopyrrolo[3,4-c]pyrrole-based oligothiophenes: Synthesis and characterization of aggregated states in solution and thin films

In this work, we synthesized a family of three structurally related chiral oligothiophenes containing a 1,4-diketo-3,6-diarylpyrrolo[3,4-c]pyrrole (DPP) unit as the central core; functionalized with the same (S)-3,7-dimethyl-1-octyl chains on the nitrogen atoms of lactam moieties, they only differ in the number of lateral thiophene units. The aggregation modes of these π-conjugated chiral systems were evaluated by means of UV-Vis absorption and ECD spectroscopies in conditions of solution aggregation (CHCl3 /MeOH mixtures) and as thin films, describing in particular the impact of the π-conjugation length on the chiroptical properties. Interestingly, we found that the variable number of thiophene units attached to the DPP core affects not only the propensity to aggregation but also the aggregates' helicity. ECD revealed information about the supramolecular arrangement of these molecules, that one would not obtain by using conventional optical spectroscopy and microscopy techniques. Thin film samples revealed very different aggregation modes with respect to solution aggregates, casting doubts on the common assumption that these latter may serve as simple models of the former ones.

Preparation of Dye Semiconductors via Coupling Polymerization Catalyzed by Two Catalysts and Application to Transistor

Organic dye semiconductors have received increasing attention as the next generation of semiconductors, and one of their potential applications is as a core component of organic transistors. In this study, two novel diketopyrrolopyrrole (DPP) dye core-based materials were designed and separately prepared using Stille coupling reactions under different palladium catalyst conditions. The molecular weights and elemental compositions were tested to demonstrate that both catalysts could be used to successfully prepare materials of this structure, with the main differences being the weight-average molecular weight and the dispersion index. PDPP-2Py-2Tz I with a longer conjugation length exhibited better thermodynamic stability than the counterpart polymer PDPP-2Py-2Tz II. The intrinsic optical properties of the polymers were relatively similar, while the electrochemical tests showed small differences in their energy levels. The polymers obtained with different catalysts displayed similar and moderate electron mobility in transistor devices, while PDPP-2Py-2Tz I possessed a higher switching ratio. Our study provides a comparison of such dye materials under different catalytic conditions and also demonstrates the great potential of dye materials for optoelectronic applications.

V-Shaped Tröger Oligothiophenes Boost Triplet Formation by CT Mediation and Symmetry Breaking

A new family of molecules obtained by coupling Tröger's base unit with dicyanovinylene-terminated oligothiophenes of different lengths has been synthesized and characterized by steady-state stationary and transient time-resolved spectroscopies. Quantum chemical calculations allow us to interpret and recognize the properties of the stationary excited states as well as the time-dependent mechanisms of singlet-to-triplet coupling. The presence of the diazocine unit in Tröger's base derivatives is key to efficiently producing singlet-to-triplet intersystem crossing mediated by the role of the nitrogen atoms and of the almost orthogonal disposition of the two thiophene arms. Spin-orbit coupling-mediated interstate intersystem crossing (ISC) is activated by a symmetry-breaking process in the first singlet excited state with partial charge transfer character. This mechanism is a characteristic of these molecular triads since the independent dicyanovinylene-oligothiophene branches do not display appreciable ISC. These results show how Tröger's base coupling of organic chromophores can be used to improve the ISC efficiency and tune their photophysics.

Dissecting the Interplay between Organic Charge-Modulated Field-Effect Transistors and Field-Effect Transistors through Interface Control Engineering

Organic charge-modulated field-effect transistors (OCMFETs) have garnered significant interest as sensing platforms for diverse applications that include biomaterials and chemical sensors owing to their distinct operational principles. This study aims to improve the understanding of driving mechanisms in OCMFETs and optimize their device performance by investigating the correlation between organic field-effect transistors (OFETs) and OCMFETs. By introducing self-assembled monolayers (SAMs) with different functional groups on the AlOx gate dielectric surface, we explored the impact of the surface characteristics on the electrical behavior of both devices. Our results indicate that the dipole moment of the dielectric surface is a critical control variable in the performance correlation between OFET and OCMFET devices, as it directly impacts the generation of the induced floating gate voltage through the control gate voltage. The insights obtained from this study contribute to the understanding of the factors affecting OCMFET performance and emphasize their potential as platforms for diverse sensing systems.

Exploring the Influence of P3HT on PTCA Crystallization and Phase Behavior in Thin Films

The thermal properties and alignment of crystallinity of materials in thin films play crucial roles in the performance and reliability of various devices, especially in the fields of electronics, materials science, and engineering. The slight variations in the molecular packing of the active layer can make considerable differences in the optical and thermal properties. Herein, we aim to investigate the tuning of the physical properties of a blended thin film of n-type small organic molecules of perylene-3,4,9,10-tetracarboxylic acid (PTCA-SMs) with the mixing of the p-type polymer poly(3-hexylthiophene) (P3HT). The resulting thin films exhibit an enhanced surface crystallinity compared to the pristine material, leading to the formation of long crystallites, and these crystallites are thermally stable in the solid state, as confirmed by X-ray diffraction (XRD), atomic force microscopy (AFM), and thermal analysis using variable-temperature spectroscopic ellipsometry (VTSE) and differential scanning calorimetry (DSC). We believe that the crystalline structure of the obtained P3HT/PTCA-SMs blends is a combination of edge-on and face-on orientations, which enable the potential use of this material as an active layer in organic electronics.

Stable peri-Naphthoisatogens without C2 Protection: Synthesis via Aldrone Condensation, Optical Properties and 1,3-Dipolar Cycloaddition Reaction

peri-Annulation of naphthalane, an important tool for realization of wide range of functional materials, is presently accomplished with limited few functional groups like imide, amide and diamine-derivative (perimidine). To increase the diversity, we have incorporated α-keto aldonitrone as a new functional group, and herein report about five peri-naphthoisatogens (PNTIs) dyes. The synthesis were accomplished using an unusual reaction of aromatic nitro group, which is nucleophilic attack of a C-nucleophile (enol) to the N-atom of nitro group. In five different 5-alkylamino-8-nitro-1-acetylnaphthalenes, intramolecular acid-catalyzed nucleophilic attack of enol moiety to the N-atom of nitro group produced α-keto aldonitrone via addition-elimination mechanism. The PNTIs showed characteristics of 1,3-dipole and reacted with ethyl acrylate to produce isoxazolidine ring, which subsequently converted into aza phenalenone derivative via ring cleavage. Both the PNTI and the corresponding derivative strongly absorb in the visible region, displaying absorption maximum at 551 and 561 nm (in CHCl3 ) respectively. Compared to the popular analogous dye naphthalene monoimides, PNTIs showed bathochromic shift of absorption maximum by more than 100 nm. The emission maximum for the PNTI and its derivative in chloroform were observed at 594 and 635 nm respectively.

Machine learning of atomic force microscopy images of organic solar cells

The bulk heterojunction structures of organic photovoltaics (OPVs) have been overlooked in their machine learning (ML) approach despite their presumably significant impact on power conversion efficiency (PCE). In this study, we examined the use of atomic force microscopy (AFM) images to construct an ML model for predicting the PCE of polymer : non-fullerene molecular acceptor OPVs. We manually collected experimentally observed AFM images from the literature, applied data curing and performed image analyses (fast Fourier transform, FFT; gray-level co-occurrence matrix, GLCM; histogram analysis, HA) and ML linear regression. The accuracy of the model did not considerably improve even by including AFM data in addition to the chemical structure fingerprints, material properties and process parameters. However, we found that a specific spatial wavelength of FFT (40-65 nm) significantly affects PCE. The GLCM and HA methods, such as homogeneity, correlation and skewness expand the scope of image analysis and artificial intelligence in materials science research fields.

Physics-inspired machine learning of localized intensive properties

Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a 'local energy'-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.

A review on accelerated development of skin-like MXene electrodes: from experimental to machine learning

Foreshadowing future needs has catapulted the progress of skin-like electronic devices for human-machine interactions. These devices possess human skin-like properties such as stretchability, self-healability, transparency, biocompatibility, and wearability. This review highlights the recent progress in a promising material, MXenes, to realize soft, deformable, skin-like electrodes. Various structural designs, fabrication strategies, and rational guidelines adopted to realize MXene-based skin-like electrodes are outlined. We explicitly discussed machine learning-based material informatics to understand and predict the properties of MXenes. Finally, an outlook on the existing challenges and the future roadmap to realize soft skin-like MXene electrodes to facilitate technological advances in the next-generation human-machine interactions has been described.

Recent advances in n-type and ambipolar organic semiconductors and their multi-functional applications

Organic semiconductors have received broad attention and research interest due to their unique integration of semiconducting properties with structural tunability, intrinsic flexibiltiy and low cost. In order to meet the requirements of organic electronic devices and their integrated circuits, p-type, n-type and ambipolar organic semiconductors are all necessary. However, due to the limitation in both material synthesis and device fabrication, the development of n-type and ambipolar materials is quite behind that of p-type materials. Recent development in synthetic methods of organic semiconductors greatly enriches the range of n-type and ambipolar materials. Moreover, the newly developed materials with multiple functions also put forward multi-functional device applications, including some emerging research areas. In this review, we give a timely summary on these impressive advances in n-type and ambipolar organic semiconductors with a special focus on their synthesis methods and advanced materials with enhanced properties of charge carrier mobility, integration of high mobility and strong emission and thermoelectric properties. Finally, multi-functional device applications are further demonstrated as an example of these developed n-type and ambipolar materials.

Flexible biomimetic block copolymer composite for temperature and long-wave infrared sensing

Biological compounds often provide clues to advance material designs. Replicating their molecular structure and functional motifs in artificial materials offers a blueprint for unprecedented functionalities. Here, we report a flexible biomimetic thermal sensing (BTS) polymer that is designed to emulate the ion transport dynamics of a plant cell wall component, pectin. Using a simple yet versatile synthetic procedure, we engineer the physicochemical properties of the polymer by inserting elastic fragments in a block copolymer architecture, making it flexible and stretchable. The thermal response of our flexible polymer outperforms current state-of-the-art temperature sensing materials, including vanadium oxide, by up to two orders of magnitude. Thermal sensors fabricated from these composites exhibit a sensitivity that exceeds 10 mK and operate stably between 15° and 55°C, even under repeated mechanical deformations. We demonstrate the use of our flexible BTS polymer in two-dimensional arrays for spatiotemporal temperature mapping and broadband infrared photodetection.

Connecting the dots for fundamental understanding of structure-photophysics-property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism

Photoactive organic and hybrid organic-inorganic materials such as conjugated polymers, covalent organic frameworks (COFs), metal-organic frameworks (MOFs), and layered perovskites, display intriguing photophysical signatures upon interaction with light. Elucidating structure-photophysics-property relationships across a broad range of functional materials is nontrivial and requires our fundamental understanding of the intricate interplay among excitons (electron-hole pair), polarons (charges), bipolarons, phonons (vibrations), inter-layer stacking interactions, and different forms of structural and conformational defects. In parallel with electronic structure modeling and data-driven science that are actively pursued to successfully accelerate materials discovery, an accurate, computationally inexpensive, and physically-motivated theoretical model, which consistently makes quantitative connections with conceptually complicated experimental observations, is equally important. Within this context, the first part of this perspective highlights a unified theoretical framework in which the electronic coupling as well as the local coupling between the electronic and nuclear degrees of freedom can be efficiently described for a broad range of quasiparticles with similarly structured Holstein-style vibronic Hamiltonians. The second part of this perspective discusses excitonic and polaronic photophysical signatures in polymers, COFs, MOFs, and perovskites, and attempts to bridge the gap between different research fields using a common theoretical construct - the Multiparticle Holstein Formalism. We envision that the synergistic integration of state-of-the-art computational approaches with the Multiparticle Holstein Formalism will help identify and establish new, transformative design strategies that will guide the synthesis and characterization of next-generation energy materials optimized for a broad range of optoelectronic, spintronic, and photonic applications.

PyThinFilm: Automated Molecular Dynamics Simulation Protocols for the Generation of Thin Film Morphologies

The performance of organic optoelectronic devices, such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), is intrinsically related to the molecular-scale morphology of the thin films from which they are composed. However, the experimental characterization of morphology at the molecular level is challenging due to the often amorphous or at best semicrystalline nature of these films. Classical molecular modeling techniques, such as molecular dynamics (MD) simulation, are increasingly used to understand the relationship between morphology and the properties of thin-film devices. PyThinFilm (github.com/ATB-UQ/PyThinFilm) is an open-source Python package which allows fully automated MD simulations of thin film growth to be performed using vacuum and/or solution deposition processes. PyThinFilm utilizes the GROMACS simulation package in combination with interaction parameters from the Automated Topology Builder (atb.uq.edu.au). Here, PyThinFilm is described along with an overview of applications in which PyThinFilm has been used to study the thin films of organic semiconductor materials typically used in OLEDs and OSCs.

Wijaya JB, Grünewald F, de Vries AH, Marrink SJ, Broer R, Havenith RWA. Strategies for Enhancing the Dielectric Constant of Organic Materials

High dielectric constant organic semiconductors, often obtained by the use of ethylene glycol (EG) side chains, have gained attention in recent years in the efforts of improving the device performance for various applications. Dielectric constant enhancements due to EGs have been demonstrated extensively, but various effects, such as the choice of the particular molecule and the frequency and temperature regime, that determine the extent of this enhancement require further understanding. In this work, we study these effects by means of polarizable molecular dynamics simulations on a carefully selected set of fullerene derivatives with EG side chains. The selection allows studying the dielectric response in terms of both the number and length of EG chains and also the choice of the group connecting the fullerene to the EG chain. The computed time- and frequency-dependent dielectric responses reveal that the experimentally observed rise of the dielectric constant within the kilo/megahertz regime for some molecules is likely due to the highly stretched dielectric response of the EGs: the initial sharp increase over the first few nanoseconds is followed by a smaller but persistent increase in the range of microseconds. Additionally, our computational protocol allows the separation of different factors that contribute to the overall dielectric constant, providing insights to make several molecular design guides for future organic materials in order to enhance their dielectric constant further.

Aggregation Modes of Chiral Diketopyrrolo[3,4‐ c ]pyrrole Dyes in Solution and Thin Films

The chiroptical features of chiral diketopyrrolo[3,4‐c]pyrrole (DPP) derivatives have been only marginally investigated to date. In this regard, we have synthesized ad hoc four chiral DPP dyes, functionalized with enantiopure alkyl groups from natural sources either on the lactam moieties or on the terminal positions of the π‐conjugated backbone, to promote an efficient self‐assembly into chiral supramolecular structures. For each of them, the aggregation modes has been investigated by absorbance and ECD spectroscopies in conditions of solution aggregation and on thin films, considering the effects of deposition technique (drop casting vs. spin coating) and post‐deposition operations (solvent and thermal annealing). The effect of the structure of lateral π‐conjugated units attached to the central DPP scaffold, as well as that of the position of the alkyl chiral group, has been assessed. ECD revealed superior capability, compared to absorbance spectroscopy, to provide information on the aggregation modes and to detect the possible co‐existence of multiple aggregation pathways.

Systematic kMC Study of Doped Hole Injection Layers in Organic Electronics

Organic light emitting diodes (OLED) play an important role in commercial displays and are promising candidates for energy-efficient lighting applications. Although they have been continuously developed since their discovery in 1987, some unresolved challenges remain. The performance of OLEDs is determined by a multifaceted interplay of materials and device architectures. A commonly used technique to overcome the charge injection barrier from the electrodes to the organic layers, are doped injection layers. The optimization of doped injection layers is critical for high-efficiency OLED devices, but has been driven mainly by chemical intuition and experimental experience, slowing down the progress in this field. Therefore, computer-aided methods for material and device modeling are promising tools to accelerate the device development process. In this work, we studied the effect of doped hole injection layers on the injection barrier in dependence on material and layer properties by using a parametric kinetic Monte Carlo model. We were able to quantitatively elucidate the influence of doping concentration, material properties, and layer thickness on the injection barrier and device conductivity, leading to the conclusion that our kMC model is suitable for virtual device design.

Charge transport in liquid crystal network of terthiophene-siloxane block molecules

We report that mesogens consisting of a terthiophene core and discrete oligodimethylsiloxane side-chains terminated by acrylate units can be photopolymerized in the columnar phase with retention of nanoscale order and charge transport capabilities.

Symmetric Spirenes: Promising Building Blocks for New Generation Opto-Electronic Materials

The spiroconjugated hydrocarbons, and in particular spirenes, show unique properties among the numerous π-conjugated materials; they are quite rigid systems, with a subsequently small excitation reorganisation energy, and hence are...

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

Nonfullerene, a small molecular electron acceptor, has substantially improved the power conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom of π-conjugated polymers and molecules makes it difficult to explore with limited resources. Machine learning, which is based on rapidly growing artificial intelligence technology, is a high-throughput method to accelerate the speed of material design and process optimization; however, it suffers from limitations in terms of prediction accuracy, interpretability, data collection, and available data (particularly, experimental data). This recognition motivates the present Perspective, which focuses on utilizing the experimental data set for ML to efficiently aid OPV research. This Perspective discusses the trends in ML-OPV publications, the NFA category, and the effects of data size and explanatory variables (fingerprints or Mordred descriptors) on the prediction accuracy and explainability, which broadens the scope of ML and would be useful for the development of next-generation solar cell materials.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers

Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al₂O₃ passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H₂ plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications.

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.

De Novo Simulation of Charge Transport through Organic Single-Carrier Devices

In amorphous organic semiconductor devices, electrons and holes are transported through layers of small organic molecules or polymers. The overall performance of the device depends both on the material and the device configuration. Measuring a single device configuration requires a large effort of synthesizing the molecules and fabricating the device, rendering the search for promising materials in the vast molecular space both nontrivial and time-consuming. This effort could be greatly reduced by computing the device characteristics from the first principles. Here, we compute transport characteristics of unipolar single-layer devices of prototypical hole- and electron-transporting materials, N,N'-di(1-naphthyl)-N,N'-diphenyl-(1,1'-biphenyl)-4,4'-diamine (α-NPD) and 2,2',2″-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi) using a first-principles multiscale approach that requires only the molecular constituents and the device geometry. This approach of generating a digital twin of the entire device can be extended to multilayer stacks and enables the computer design of materials and devices to facilitate systematic improvement of organic light-emitting diode (OLED) devices.

Synthesis of Aza[n]phenacenes (n = 4-6) via Photocyclodehydrochlorination of 2-Chloro-N-aryl-1-naphthamides

A novel methodology for the synthesis of aza[n]phenacenes was successfully developed utilizing photocyclodehydrochlorination reaction of 2-chloro-N-aryl-1-naphthamides. In these key intermediates, the factors influencing the photoreaction were studied. The target aza[n]phenacenes were obtained by triflation or chlorination from prepared phenanthridinones, followed by hydrogenation. The introduction of a nitrogen atom into a phenacene skeleton induced changes in the physicochemical properties. The important properties of prepared aza[n]phenacenes (n = 4-6) were studied experimentally and by density functional theory calculations and were compared to those of their carbo analogues. Furthermore, some important features of the crystalline aza[n]phenacenes were investigated, including intermolecular interaction in the crystal lattice and the increased solubility or decreased melting points.

Computing Charging and Polarization Energies of Small Organic Molecules Embedded into Amorphous Materials with Quantum Accuracy

The ionization potential, electron affinity, and cation/anion polarization energies (IP, EA, P⁽⁺⁾, P^(-)) of organic molecules determine injection barriers, charge carriers balance, doping efficiency, and light outcoupling in organic electronics devices, such as organic light-emitting diodes (OLEDs). Computing IP and EA of isolated molecules is a common task for quantum chemistry methods. However, once molecules are embedded in an amorphous organic matrix, IP and EA values change, and accurate predictions become challenging. Here, we present a revised quantum embedding method [Friederich et al. J. Chem. Theory Comput. 2014, 10 (9), 3720-3725] that accurately predicts the dielectric permittivity and ionization potentials in three test materials, NPB, TCTA, and C60, and allows straightforward interpretation of their nature. The method paves the way toward reliable virtual screening of amorphous organic semiconductors with targeted IP/EA, polarization energies, and relative dielectric permittivity.

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning

Organic semiconductors are indispensable for today's display technologies in the form of organic light-emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long timescales are required, which is the case to compute static and dynamic energy disorder, i.e., the dominant factor to determine charge transport. Here, we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps are highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyze characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application-relevant materials properties of organic semiconductors and thus make these methods applicable for virtual materials design.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Multi-level aggregation of conjugated small molecules and polymers: from morphology control to physical insights

Aggregation of molecules is a multi-molecular phenomenon occurring when two or more molecules behave differently from discrete molecules due to their intermolecular interactions. Moving beyond single molecules, aggregation usually demonstrates evolutive or wholly emerging new functionalities relative to the molecular components. Conjugated small molecules and polymers interact with each other, resulting in complex solution-state aggregates and solid-state microstructures. Optoelectronic properties of conjugated small molecules and polymers are sensitively determined by their aggregation states across a broad range of spatial scales. This review focused on the aggregation ranging from molecular structure, intermolecular interactions, solution-state assemblies, and solid-state microstructures of conjugated small molecules and polymers. We addressed the importance of such aggregation in filling the gaps from the molecular level to device functions and highlighted the multi-scale structures and properties at different scales. From the view of multi-level aggregation behaviors, we divided the whole process from the molecule to devices into several parts: molecular design, solvation, solution-state aggregation, crystal engineering, and solid-state microstructures. We summarized the progress and challenges of relationships between optoelectronic properties and multi-level aggregation. We believe aggregation science will become an interdisciplinary research field and serves as a general platform to develop future materials with the desired functions.

The Cascade Reactions of Indigo with Propargyl Substrates for Heterocyclic and Photophysical Diversity

The synthesis of structurally diverse heterocycles for chemical space exploration was achieved via the cascade reactions of indigo with propargylic electrophiles. New pyrazinodiindolodione, naphthyridinedione, azepinodiindolone, oxazinoindolone and pyrrolodione products were prepared in one pot reactions by varying the leaving group (-Cl, -Br, -OMs, -OTs) or propargyl terminal functionality (-H, -Me, -Ph, -Ar). Mechanistic and density functional theory studies revealed that the unsaturated propargyl moiety can behave as an electrophile when aromatic terminal substitutions are made, and therefore competes with leaving group substitution for new outcomes. Selected products from the cascade reactions were investigated for their absorption and fluorescence properties, including transient absorption spectroscopy. This revealed polarity dependent excited state relaxation pathways, fluorescence, and triplet formation, thus highlighting these reactions as a means to access diverse functional materials rapidly.

Coarse-Graining Organic Semiconductors: The Path to Multiscale Design

Four decades of molecular theory and computation have helped form the modern understanding of the physical chemistry of organic semiconductors. Whereas these efforts have historically centered around characterizations of electronic structure at the single-molecule or dimer scale, emerging trends in noncrystalline molecular and polymeric semiconductors are motivating the need for modeling techniques capable of morphological and electronic structure predictions at the mesoscale. Provided the challenges associated with these prediction tasks, the community has begun to evolve a computational toolkit for organic semiconductors incorporating techniques from the fields of soft matter, coarse-graining, and machine learning. Here, we highlight recent advances in coarse-grained methodologies aimed at the multiscale characterization of noncrystalline organic semiconductors. As organic semiconductor performance is dependent on the interplay of mesoscale morphology and molecular electronic structure, specific emphasis is placed on coarse-grained modeling approaches capable of both structural and electronic predictions without recourse to all-atom representations.

Impact of chirality on the aggregation modes of l-phenylalanine- and d-glucose-decorated phenylene–thiophene oligomers

Three l-phenylalanine- or d-glucose-decorated phenylene–thiophene oligomers have been studied using UV-Vis and ECD spectroscopies in different conditions of solution aggregation and thin films, investigating the impact of chirality on their aggregation modes.

High-performance polymer field-effect transistors: from the perspective of multi-level microstructures

The multi-level microstructure of conjugated polymers is the most critical parameter determining the charge transport property in field-effect transistors (FETs). However, controlling the hierarchical microstructures and the structural evolution remains a significant challenge. In this perspective, we discuss the key aspects of multi-level microstructures of conjugated polymers towards high-performance FETs. We highlight the recent progress in the molecular structures, solution-state aggregation, and polymer crystal structures, representing the multi-level microstructures of conjugated polymers. By tuning polymer hierarchical microstructures, we attempt to provide several guidelines for developing high-performance polymer FETs and polymer electronics.

Chiral Oligothiophenes with Remarkable Circularly Polarized Luminescence and Electroluminescence in Thin Films

This work reports the first observation of circularly polarized electroluminescence (CPEL) in thin films of self-organized oligothiophenes. Four new 1,4-phenylene and 9H-carbazole-based oligothiophenes were ad hoc designed to ensure efficient spontaneous formation of chiral supramolecular order. They were easily synthesized and their chiroptical properties in thin films were measured. Circularly polarized luminescence (CPL) spectra revealed g_lum in the order of 10^-2 on a wide wavelengths range, originating from their self-organized chiral supramolecular organization. These molecules have reasonable properties as organic semiconductors and for this reason they can constitute the active layer of circularly-polarized organic light-emitting diodes (CP-OLEDs). Thus, we could investigate directly their electroluminescence (EL) and CPEL, without resorting to blends, but rather in a simple multilayer device with basic architecture. This is the first example of a CP-OLED with active layer made only of a small organic compound.

The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study

Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r 2 > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers.

Designing and understanding light-harvesting devices with machine learning

Understanding the fundamental processes of light-harvesting is crucial to the development of clean energy materials and devices. Biological organisms have evolved complex metabolic mechanisms to efficiently convert sunlight into chemical energy. Unraveling the secrets of this conversion has inspired the design of clean energy technologies, including solar cells and photocatalytic water splitting. Describing the emergence of macroscopic properties from microscopic processes poses the challenge to bridge length and time scales of several orders of magnitude. Machine learning experiences increased popularity as a tool to bridge the gap between multi-level theoretical models and Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices.

Configurationally Random Polythiophene for Improved Polymer Ordering and Charge-Transporting Ability

Random polythiophene polymers are characterized by the arbitrary sequences of monomeric units along polymer backbones. These untailored orientations generally result in the twisting of thiophene rings out of the conjugation planarity in addition to steric repulsions experienced among substituted alkyl chains. These tendencies have limited close polymer packing, which has been detrimental to charge transport in these moieties. To ameliorate charge transport in these classes of polymers, we make use of simple Stille coupling polymerization to synthesize highly random polythiophene polymers. We induced a positive microstructural change between polymer chains by attuning the ratio between alkyl-substituted and nonalkyl-substituted monomer units along the backbones. The optimized random polythiophene was found to have enhanced intermolecular interaction, increased size of crystallites, and stronger tendency to take edge orientation compared with both regiorandom and regioregular poly(3-hexylthiophene) polymers. Incorporation of the optimized random polythiophene as an active material in solid-state electrolyte-gated organic field-effect transistors exhibited better performance than the control device using regioregular poly(3-hexylthiophene), with a high hole mobility up to 4.52 cm² V^-1 s^-1 in ambient conditions.

Chiroptical Properties in Thin Films of π-Conjugated Systems

Chiral π-conjugated molecules provide new materials with outstanding features for current and perspective applications, especially in the field of optoelectronic devices. In thin films, processes such as charge conduction, light absorption, and emission are governed not only by the structure of the individual molecules but also by their supramolecular structures and intermolecular interactions to a large extent. Electronic circular dichroism, ECD, and its emission counterpart, circularly polarized luminescence, CPL, provide tools for studying aggregated states and the key properties to be sought for designing innovative devices. In this review, we shall present a comprehensive coverage of chiroptical properties measured on thin films of organic π-conjugated molecules. In the first part, we shall discuss some general concepts of ECD, CPL, and other chiroptical spectroscopies, with a focus on their applications to thin film samples. In the following, we will overview the existing literature on chiral π-conjugated systems whose thin films have been characterized by ECD and/or CPL, as well other chiroptical spectroscopies. Special emphasis will be put on systems with large dissymmetry factors (g_abs and g_lum) and on the application of ECD and CPL to derive structural information on aggregated states.