Active discovery of organic semiconductors

High Throughput Characterization of Organic Thin Film Transistors

Automation is vital to accelerating research. In recent years, the application of self-driving labs to materials discovery and device optimization has highlighted many benefits and challenges inherent to these new technologies. Successful automated workflows offer tangible benefits to fundamental science and industrial scale-up by significantly increasing productivity and reproducibility all while enabling entirely new types of experiments. However, it's implemtation is often time-consuming and cost-prohibitive and necessitates establishing multidisciplinary teams that bring together domain-specific knowledge with specific skillsets in computer science and engineering. This perspective article provides a comprehensive overview of how the research group has adopted "hybrid automation" over the last 8 years by using simple automatic electrical testers (autotesters) as a tool to increase productivity and enhance reproducibility in organic thin film transistor (OTFT) research. From wearable and stretchable electronics to next-generation sensors and displays, OTFTs have the potential to be a key technology that will enable new applications from health to aerospace. The combination of materials chemistry, device manufacturing, thin film characterization and electrical engineering makes OTFT research challenging due to the large parameter space created by both diverse material roles and device architectures. Consequently, this research stands to benefit enormously from automation. By leveraging the multidisciplinary team and taking a user-centered design approach in the design and continued improvement of the autotesters, the group has meaningfully increased productivity, explored research avenues impossible with traditional workflows, and developed as scientists and engineers capable of effectively designing and leveraging automation to build the future of their fields to encourage this approach, the files for replicating the infrastructure are included, and questions and potential collaborations are welcomed.

MolPackL: Quantification and Interpretation of Intermolecular Interactions Driven by Molecular Packing

In organic optoelectronic devices, the properties of the aggregated organic materials depend not only on individual molecules or monomers but also significantly on their packing modes. Different from their inorganic counterparts linked by explicit covalent bonds, organic solids exhibit intricate and numerous intermolecular interactions (IMIs). Due to the intrinsic complexity and disorder of IMIs, identifying and understanding them is a formidable challenge in experimental, theoretical, and data-driven approaches. In this work, we constructed an innovative algorithm framework, Molecular Packing Learning (MolPackL), which can accurately quantify elusive IMIs using contact density histograms (CDHs) and efficiently extract intermolecular features for further property prediction of organic solids. It performs satisfactorily in training predictive models of IMI-related properties in molecular crystals. Particularly, the band gap predictive model based on MolPackL achieved the best-reported performance, with an MAE of 0.20 eV and an impressive R2 of 0.92. Class activation mapping (CAM) visually demonstrates MolPackL's accurate identification of effective interaction sites as the molecular packing changes. What is more, the elemental importance analysis verified that the superior score benefits from MolPackL's ability to comprehensively consider multiple influencing factors of IMIs. In summary, MolPackL provides a new framework for quantitative assessment and understanding of the effect of IMIs. The development of MolPackL marks a significant advancement in establishing predictive models of molecular aggregates, deepening the comprehension of IMIs on the material properties. Given the superior performance, we believe that MolPackL will also become a powerful tool in the design of high-performance organic optoelectronic materials.

Designing building blocks of covalent organic frameworks through on-the-fly batch-based Bayesian optimization

In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units-called building blocks (BBs)-are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles. In order to ensure valid BBs, we use a molecular generation algorithm that, by construction, leads to rigid three-fold symmetric molecules. The BO approach then trains two distinct surrogate models for two conductivity properties, level alignment vs a reference electrode and reorganization free energy, which are combined in a fitness function as the objective that evaluates BBs' conductivities. These continuously improving surrogates allow the prediction of a material's properties at a low computational cost. It thus allows us to select promising candidates which, together with candidates that are very different from the molecules already sampled, form the updated training sets of the surrogate models. In the course of 20 such training steps, we find a number of promising candidates, some being only variations on already known motifs and others being completely novel. Finally, we subject the six best such candidates to a computational reverse synthesis analysis to gauge their real-world synthesizability.

Active learning streamlines development of high performance catalysts for higher alcohol synthesis

Developing efficient catalysts for syngas-based higher alcohol synthesis (HAS) remains a formidable research challenge. The chain growth and CO insertion requirements demand multicomponent materials, whose complex reaction dynamics and extensive chemical space defy catalyst design norms. We present an alternative strategy by integrating active learning into experimental workflows, exemplified via the FeCoCuZr catalyst family. Our data-aided framework streamlines navigation of the extensive composition and reaction condition space in 86 experiments, offering >90% reduction in environmental footprint and costs over traditional programs. It identifies the Fe65Co19Cu5Zr11 catalyst with optimized reaction conditions to attain higher alcohol productivities of 1.1 gHA h-1 gcat-1 under stable operation for 150 h on stream, a 5-fold improvement over typically reported yields. Characterization reveals catalytic properties linked to superior activities despite moderate higher alcohol selectivities. To better reflect catalyst demands, we devise multi-objective optimization to maximize higher alcohol productivity while minimizing undesired CO2 and CH4 selectivities. An intrinsic trade-off between these metrics is uncovered, identifying Pareto-optimal catalysts not readily discernible by human experts. Finally, based on feature-importance analysis, we formulate data-informed guidelines to develop performance-specific FeCoCuZr systems. This approach goes beyond existing HAS catalyst design strategies, is adaptable to broader catalytic transformations, and fosters laboratory sustainability.

COMPAS-3: a dataset of peri-condensed polybenzenoid hydrocarbons

We introduce the third installment of the COMPAS Project - a COMputational database of Polycyclic Aromatic Systems, focused on peri-condensed polybenzenoid hydrocarbons. In this installment, we develop two datasets containing the optimized ground-state structures and a selection of molecular properties of ∼39k and ∼9k peri-condensed polybenzenoid hydrocarbons (at the GFN2-xTB and CAM-B3LYP-D3BJ/cc-pvdz//CAM-B3LYP-D3BJ/def2-SVP levels, respectively). The manuscript details the enumeration and data generation processes and describes the information available within the datasets. An in-depth comparison between the two types of computation is performed, and it is found that the geometrical disagreement is maximal for slightly-distorted molecules. In addition, a data-driven analysis of the structure-property trends of peri-condensed PBHs is performed, highlighting the effect of the size of peri-condensed islands and linearly annulated rings on the HOMO-LUMO gap. The insights described herein are important for rational design of novel functional aromatic molecules for use in, e.g., organic electronics. The generated datasets provide a basis for additional data-driven machine- and deep-learning studies in chemistry.

Statistical analysis of interatomic transfer integrals for exploring high-mobility organic semiconductors

Charge transport in organic semiconductors occurs via overlapping molecular orbitals quantified by transfer integrals. However, no statistical study of transfer integrals for a wide variety of molecules has been reported. Here we present a statistical analysis of transfer integrals for more than 27,000 organic compounds in the Cambridge Structural Database. Interatomic transfer integrals were used to identify substructures with high transfer integrals. As a result, thione and amine groups as in thiourea were found to exhibit high transfer integrals. Such compounds are considered as potential non-aromatic, water-soluble organic semiconductors.

Organic Donor-Acceptor Complexes As Potential Semiconducting Materials

In this review article, the synthesis, characterization and physico-chemical properties of the organic donor-acceptor complexes are highlighted and a special emphasis has been placed on developing them as semiconducting materials. The electron-rich molecules, i. e., donors have been broadly grouped in three categories, namely polycyclic aromatic hydrocarbons, nitrogen heterocycles and sulphur containing aromatic donors. The reactions of these classes of the donors with the acceptors, namely tetracyanoquinodimethane (TCNQ), tetracyanoethylene (TCNE), tetracyanobenzene (TCNB), benzoquinone, pyromellitic dianhydride and pyromellitic diimides, fullerenes, phenazine, benzothiadiazole, naphthalimide, DMAD, maleic anhydride, viologens and naphthalene diimide are described. The potential applications of the resulting DA complexes for physico-electronic purposes are also included. The theoretical investigation of many of these products with a view to rationalise their observed physico-chemical properties is also discussed.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.

Machine-learning-assisted performance improvements for multi-resonance thermally activated delayed fluorescence molecules

With favorable colour purity, multi-resonance thermally activated delayed fluorescence (MR-TADF) molecules exhibit enormous potential in high-definition displays. Due to the relatively small chemical space of MR-TADF molecules, it is challenging to improve molecular performance through domain-specific expertise alone. To address this problem, we focused on optimizing the classic molecule, DABNA-1, using machine learning (ML). Molecular morphing operations were initially employed to generate the adjacent chemical space of DABNA-1. Subsequently, a machine learning model was trained with a limited database and used to predict the properties throughout the generated chemical space. It was confirmed that the top 100 molecules suggested by machine learning present excellent electronic structures, characterized by small reorganization energy and singlet-triplet energy gaps. Our results indicate that the improvement in electronic structures can be elucidated through the view of the molecular orbital (MO). The results also reveal that the top 5 molecules present weaker vibronic peaks of the emission spectrum, demonstrating higher colour purity when compared to DABNA-1. Notably, the M2 molecule presents a high RISC rate, indicating its promising future as a high-efficiency MR-TADF molecule. Our machine-learning-assisted approach facilitates the rapid optimization of classical molecules, addressing a crucial requirement within the organic optoelectronic materials community.

Organic Electronics in Biosensing: A Promising Frontier for Medical and Environmental Applications

The promising field of organic electronics has ushered in a new era of biosensing technology, thus offering a promising frontier for applications in both medical diagnostics and environmental monitoring. This review paper provides a comprehensive overview of organic electronics' remarkable progress and potential in biosensing applications. It explores the multifaceted aspects of organic materials and devices, thereby highlighting their unique advantages, such as flexibility, biocompatibility, and low-cost fabrication. The paper delves into the diverse range of biosensors enabled by organic electronics, including electrochemical, optical, piezoelectric, and thermal sensors, thus showcasing their versatility in detecting biomolecules, pathogens, and environmental pollutants. Furthermore, integrating organic biosensors into wearable devices and the Internet of Things (IoT) ecosystem is discussed, wherein they offer real-time, remote, and personalized monitoring solutions. The review also addresses the current challenges and future prospects of organic biosensing, thus emphasizing the potential for breakthroughs in personalized medicine, environmental sustainability, and the advancement of human health and well-being.

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO2 Monitoring

Flexible and wearable biosensors are the next-generation healthcare devices that can efficiently monitor human health conditions in day-to-day life. Moreover, the rapid growth and technological advancements in wearable optoelectronics have promoted the development of flexible organic photoplethysmography (PPG) biosensor systems that can be implanted directly onto the human body without any additional interface for efficient bio-signal monitoring. As an example, the pulse oximeter utilizes PPG signals to monitor the oxygen saturation (SpO2 ) in the blood volume using two distinct wavelengths with organic light emitting diode (OLED) as light source and an organic photodiode (OPD) as light sensor. Utilizing the flexible and soft properties of organic semiconductors, pulse oximeter can be both flexible and conformal when fabricated on thin polymeric substrates. It can also provide highly efficient human-machine interface systems that can allow for long-time biological integration and flawless measurement of signal data. In this work, a clear and systematic overview of the latest progress and updates in flexible and wearable all-organic pulse oximetry sensors for SpO2 monitoring, including design and geometry, processing techniques and materials, encapsulation and various factors affecting the device performance, and limitations are provided. Finally, some of the research challenges and future opportunities in the field are mentioned.

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.

Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator's results, to further refine the regression. This new "swarm smart" algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.

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.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Charge Localization in Acene Crystals from Ab Initio Electronic Structure

The performance of Koopmans-compliant hybrid functionals in reproducing the electronic structure of organic crystals is tested for a series of acene crystals. The calculated band gaps are found to be consistent with those achieved with the GW method at a fraction of the computational cost and in excellent accord with the experimental results at room temperature, when including the thermal renormalization. The energetics of excess holes and electrons reveals a struggle between polaronic localization and band-like delocalization. The consequences of these results on the transport properties of acene crystals are discussed.

New Luminescent Pyridine-based Disc type Molecules: Synthesis, Photophysical, Electrochemical, and DFT studies

The design and synthesis of new conjugated luminescent molecules have attracted the attention of researchers because of their various applications, especially in the field of optoelectronic devices. Most of the applications were mainly based on the intramolecular charge transfer (ICT). For this purpose, we designed and synthesized a series of new donor-acceptor based disc type molecules i.e. 2,4,6-tris(4-(alkyloxy)phenyl)pyridines carrying variable alkoxy chains [i.e. n = 2, 4, 6, 8, 10, 12, 14, 16]. Further, the structures of all the synthesized compounds were confirmed by using ATR-IR, ¹H-NMR, ¹³C-NMR, and ESI-MS analysis. Moreover, the photophysical property study indicated that all the molecules are blue light emitting materials, however the change of alkoxy chain length in phenyl arms does not affect their absorption, emission, and energy levels. Besides, the thermal study revealed that core is stable up to 350 °C. Also, the DFT study showed that the photo induced electron transfer caused by HOMO-LUMO excitation in the studied molecules. Therefore, all the molecules have potential applications in optoelectronic applications.

Conductivity in Thin Films of Transition Metal Coordination Complexes

Two coordination complexes have been made by combining the dithiolene complexes [M(mnt)2]2- (mnt = maleonitriledithiolate; M = Ni2+ or Cu2+) as anion, with the copper(II) coordination complex [Cu(Stetra)] (Stetra = 6,6'-bis(4,5-dihydrothiazol-2-yl)-2,2'-bipyri-dine) as cation. The variation of the metal centers leads to a dramatic change in the conductivity of the materials, with the M = Cu2+ variant (Cu-Cu) displaying semiconductor behavior with a conductivity of approximately 2.5 × 10-8 S cm-1, while the M = Ni2+ variant (Ni-Cu) displayed no observable conductivity. Computational studies found Cu-Cu enables a minimization of reorganization energy losses and, as a result, a lower barrier to the charge transfer process, resulting in the reported higher conductivity.

Effects of Charge Traps on Hysteresis in Organic Field-Effect Transistors and Their Charge Trap Cause Analysis through Causal Inference Techniques

Hysteresis in organic field-effect transistors is attributed to the well-known bias stress effects. This is a phenomenon in which the measured drain-source current varies when sweeping the gate voltage from on to off or from off to on. Hysteresis is caused by various factors, and one of the most common is charge trapping. A charge trap is a defect that occurs in an interface state or part of a semiconductor, and it refers to an electronic state that appears distributed in the semiconductor's energy band gap. Extensive research has been conducted recently on obtaining a better understanding of charge traps for hysteresis. However, it is still difficult to accurately measure or characterize them, and their effects on the hysteresis of organic transistors remain largely unknown. In this study, we conduct a literature survey on the hysteresis caused by charge traps from various perspectives. We first analyze the driving principle of organic transistors and introduce various types of hysteresis. Subsequently, we analyze charge traps and determine their influence on hysteresis. In particular, we analyze various estimation models for the traps and the dynamics of the hysteresis generated through these traps. Lastly, we conclude this study by explaining the causal inference approach, which is a machine learning technique typically used for current data analysis, and its implementation for the quantitative analysis of the causal relationship between the hysteresis and the traps.

Active Discovery of Donor:Acceptor Combinations For Efficient Organic Solar Cells

The structural flexibility of organic semiconductors offers vast a search space, and many potential candidates (donor and acceptor) for organic solar cells (OSCs) are yet to be discovered. Machine learning is extensively used for material discovery but performs poorly on extrapolation tasks with small training data sets. Active learning techniques can guide experimentalists to extrapolate and find the most promising D:A combination in a significantly small number of experiments. This study uses an active learning technique with a predictive random forest model to iteratively find the most optimal D:A combinations in the search space using various acquisition functions. Active learning results with five different acquisition functions (MM, MEI, MLI, MU, and UCB) are compared. Results reveal that acquisition functions that combine exploitation and exploration (MEI, MLI, and UCB) perform far better than purely exploiting (MM) and purely exploring (MU) acquisition functions. Interestingly, the proposed model can overcome the bottleneck of extrapolating small training data sets and find most promising D:A combinations in relatively fewer experiments.

Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network

Knowledge about the charge dynamics in organic light-emitting diodes (OLEDs) is a critical clue to optimize device architecture for enhancing the power efficiency and driving voltage characteristics in addition to the external quantum efficiency. In this work, we demonstrated that the charge behavior according to the operation voltage of OLEDs could be understood by introducing the convolutional neural network (CNN) of the machine learning framework without additional analysis of the unipolar charge devices. The CNN model trained using a two-dimensional (2D) modulus fingerprint simultaneously predicted the mobilities of the charge transport and emitting layers, realizing a deep understanding of the complicated data that humans cannot interpret. The machine learning model successfully describes the electrical properties of the organic layers in the actual devices configurated by different electron-transporting materials and the composition of cohosts in the emitting layer. For the first time, it was revealed that 2D fingerprints extracted using frequency- and voltage-dependent modulus spectra were effective data to represent comprehensive charge dynamics of OLEDs. The interpretation and perspective of the machine learning approach in this work were also discussed.

The superposed electric field effect on the charge transport and polaron formation in molecular crystals

We study the polaron formation and its mobility in a one-dimensional molecular crystal subjected to superposed static and harmonic electric fields. Such molecular chain exhibits intermolecular vibrational degrees of freedom, which makes the carrier-lattice interaction an important parameter of the system. By exploring field settings in which the preferential transport occurs, we show the existence of different small polaron formations, including those that travel close to the sound velocity or that are stationary by self-trapping. Electric fields magnitudes and carrier-lattice coupling have also been analyzed, which allowed to show a phase diagram that describes the existing regimes. In addition to thresholds between the mobile and stationary polaron regimes, this phase diagram unveils an unusual aspect: a metastable polaron formation.

Virtual Screening for Organic Solar Cells and Light Emitting Diodes

The field of organic semiconductors is multifaceted and the potentially suitable molecular compounds are very diverse. Representative examples include discotic liquid crystals, dye-sensitized solar cells, conjugated polymers, and graphene-based low-dimensional materials. This huge variety not only represents enormous challenges for synthesis but also for theory, which aims at a comprehensive understanding and structuring of the plethora of possible compounds. Eventually computational methods should point to new, better materials, which have not yet been synthesized. In this perspective, it is shown that the answer to this question rests upon the delicate balance between computational efficiency and accuracy of the methods used in the virtual screening. To illustrate the fundamentals of virtual screening, chemical design of non-fullerene acceptors, thermally activated delayed fluorescence emitters, and nanographenes are discussed.

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

Inverse molecular design from first principles: tailoring organic chromophore spectra for optoelectronic applications

The discovery of molecules with tailored optoelectronic properties such as specific frequency and intensity of absorption or emission is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the question: how can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties. In this perspective we address both of these challenges from first principles. We firstly adapt the Thomas-Reiche-Kuhn sum rule to organic chromophores and show how this indicates how easily the absorption and emission of a molecule can be improved. We then show how by combining electronic structure theory and intensity borrowing perturbation theory we can predict whether or not the proposed morphing operations will achieve the desired spectral alteration, and thereby derive widely-applicable design rules. We go on to provide proof-of-concept illustrations of this approach to optimizing the visible absorption of acenes and the emission of radical OLEDs. We believe this approach can be integrated into genetic algorithms by biasing morphing operations in favour of those which are likely to be successful, leading to faster molecular discovery and greener chemistry.

Charge Transport in Organic Semiconductors: The Perspective from Nonadiabatic Molecular Dynamics

Organic semiconductors (OSs) are an exciting class of materials that have enabled disruptive technologies in this century including large-area electronics, flexible displays, and inexpensive solar cells. All of these technologies rely on the motion of electrical charges within the material and the diffusivity of these charges critically determines their performance. In this respect, it is remarkable that the nature of the charge transport in these materials has puzzled the community for so many years, even for apparently simple systems such as molecular single crystals: some experiments would better fit an interpretation in terms of a localized particle picture, akin to molecular or biological electron transfer, while others are in better agreement with a wave-like interpretation, more akin to band transport in metals.

Exciting recent progress in the theory and simulation of charge carrier transport in OSs has now led to a unified understanding of these disparate findings, and this Account will review one of these tools developed in our laboratory in some detail: direct charge carrier propagation by quantum-classical nonadiabatic molecular dynamics. One finds that even in defect-free crystals the charge carrier can either localize on a single molecule or substantially delocalize over a large number of molecules depending on the relative strength of electronic couplings between the molecules, reorganization, or charge trapping energy of the molecule and thermal fluctuations of electronic couplings and site energies, also known as electron–phonon couplings.

Our simulations predict that in molecular OSs exhibiting some of the highest measured charge mobilities to date, the charge carrier forms “flickering” polarons, objects that are delocalized over 10–20 molecules on average and that constantly change their shape and extension under the influence of thermal disorder. The flickering polarons propagate through the OS by short (≈10 fs long) bursts of the wave function that lead to an expansion of the polaron to about twice its size, resulting in spatial displacement, carrier diffusion, charge mobility, and electrical conductivity. Arguably best termed “transient delocalization”, this mechanistic scenario is very similar to the one assumed in transient localization theory and supports its assertions. We also review recent applications of our methodology to charge transport in disordered and nanocrystalline samples, which allows us to understand the influence of defects and grain boundaries on the charge propagation.

Unfortunately, the energetically favorable packing structures of typical OSs, whether molecular or polymeric, places fundamental constraints on charge mobilities/electronic conductivity compared to inorganic semiconductors, which limits their range of applications. In this Account, we review the design rules that could pave the way for new very high-mobility OS materials and we argue that 2D covalent organic frameworks are one of the most promising candidates to satisfy them.

We conclude that our nonadiabatic dynamics method is a powerful approach for predicting charge carrier transport in crystalline and disordered materials. We close with a brief outlook on extensions of the method to exciton transport, dissociation, and recombination. This will bring us a step closer to an understanding of the birth, survival, and annihiliation of charges at interfaces of optoelectronic devices.

Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.

Temperature-Dependent Structural Phase Transition in Rubrene Single Crystals: The Missing Piece from the Charge Mobility Puzzle?

Accurate structural models for rubrene, the benchmark organic semiconductor, derived from synchrotron X-ray data in the temperature range of 100-300 K, show that its cofacially stacked tetracene backbone units remain blocked with respect to each other upon cooling to 200 K and start to slip below that temperature. The release of the blocked slippage occurs at approximately the same temperature as the hole mobility crossover. The blocking between 200 and 300 K is caused by a negative correlation between the relatively small thermal expansion along the crystallographic b-axis and the relatively large widening of the angle between herringbone-stacked tetracene units. DFT calculations reveal that this blocked slippage is accompanied by a discontinuity in the variation with temperature of the electronic couplings associated with hole transport between cofacially stacked tetracene backbones.

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.