Mapping the optoelectronic property space of small aromatic molecules

Accelerated Computer-Aided Screening of Optical Materials: Investigating the Potential of Δ-SCF Methods to Predict Emission Maxima of Large Dye Molecules

Accurate simulation of electronic excited states of large chromophores is often difficult due to the computationally expensive nature of existing methods. Common approximations such as fragmentation methods that are routinely applied to ground-state calculations of large molecules are not easily applicable to excited states due to the delocalized nature of electronic excitations in most practical chromophores. Thus, special techniques specific to excited states are needed. Δ-SCF methods are one such approximation that treats excited states in a manner analogous to that for ground-state calculations, accelerating the simulation of excited states. In this work, we employed the popular initial maximum overlap method (IMOM) to avoid the variational collapse of the electronic excited state orbitals to the ground state. We demonstrate that it is possible to obtain emission energies from the first singlet (S1) excited state of many thousands of dye molecules without any external intervention. Spin correction was found to be necessary to obtain accurate excitation and emission energies. Using thousands of dye-like chromophores and various solvents (12,318 combinations), we show that the spin-corrected initial maximum overlap method accurately predicts emission maxima with a mean absolute error of only 0.27 eV. We further improved the predictive accuracy using linear fit-based corrections from individual dye classes to achieve an impressive performance of 0.17 eV. Additionally, we demonstrate that IMOM spin density can be used to identify the dye class of chromophores, enabling improved prediction accuracy for complex dye molecules, such as dyads (chromophores containing moieties from two different dye classes). Finally, the convergence behavior of IMOM excited state SCF calculations is analyzed briefly to identify the chemical space, where IMOM is more likely to fail.

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.

Spectroscopic Identification of Charge Transfer of Thiolated Molecules on Gold Nanoparticles via Gold Nanoclusters

Investigation of charge transfer needs analytical tools that could reveal this phenomenon, and enables understanding of its effect at the molecular level. Here, we show how the combination of using gold nanoclusters (AuNCs) and different spectroscopic techniques could be employed to investigate the charge transfer of thiolated molecules on gold nanoparticles (AuNP@Mol). It was found that the charge transfer effect in the thiolated molecule could be affected by AuNCs, evidenced by the amplification of surface-enhanced Raman scattering (SERS) signal of the molecule and changes in fluorescence lifetime of AuNCs. Density functional theory (DFT) calculations further revealed that AuNCs could amplify the charge transfer process at the molecular level by pumping electrons to the surface of AuNPs. Finite element method (FEM) simulations also showed that the electromagnetic enhancement mechanism along with chemical enhancement determines the SERS improvement in the thiolated molecule. This study provides a mechanistic insight into the investigation of charge transfer at the molecular level between organic and inorganic compounds, which is of great importance in designing new nanocomposite systems. Additionally, this work demonstrates the potential of SERS as a powerful analytical tool that could be used in nanochemistry, material science, energy, and biomedical fields.

Recent synthetic strategies of small heterocyclic organic molecules with optoelectronic applications: a review

Over the past few years, there have been tremendous developments in the design and synthesis of organic optoelectronic materials with appealing applications in device fabrication of organic light-emitting diodes, superconductors, organic lasers, organic field-effect transistors, clean energy-producing organic solar cells, etc. There is an increasing demand for the synthesis of green, highly efficient organic optoelectronic materials to cope with the issue of efficiency roll-off in organic semiconductor-based devices. This review systematically summarized the recent progress in the design and synthesis of small organic molecules having promising optoelectronic properties for their potential applications in optoelectronic devices during the last 10-year range (2010-early 2021).

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).

High-throughput property-driven generative design of functional organic molecules

The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.

Complexities in Modeling Organic Aerosol Light Absorption

Aerosol particles dynamically evolve in the atmosphere by physicochemical interactions with sunlight, trace chemical species, and water. Current modeling approaches fix properties such as aerosol refractive index, introducing spatial and temporal errors in the radiative impacts. Further progress requires a process-level description of the refractive indices as the particles age and experience physicochemical transformations. We present two multivariate modeling approaches of light absorption by brown carbon (BrC). The initial approach was to extend the modeling framework of the refractive index at 589 nm (nD), but that result was insufficient. We developed a second multivariate model using aromatic rings and functional groups to predict the imaginary part of the complex refractive index. This second model agreed better with measured spectral absorption peaks, showing promise for a simplified treatment of BrC optics. In addition to absorption, organic functionalities also alter the water affinity of the molecules, leading to a hygroscopic uptake and increased light absorption, which we show through measurements and modeling.

Emergent Insulator-Metal Transition with Tunable Optical and Electrical Gap in Thin Films of a Molecular Conducting Composite

Composites exhibit unique synergistic properties emerging when components with different properties are combined. The tuning of the energy bandgap in the electronic structure of the material allows designing tailor-made systems with desirable mechanical, electrical, optical, and/or thermal properties. Here, we study an emergent insulator-metal transition at room temperature in bilayered (BL) thin-films comprised of polycarbonate/molecular-metal composites. Temperature-dependent resistance measurements allow monitoring of the electrical bandgap, which is in agreement with the optical bandgap extracted by optical absorption spectroscopy. The semiconductor-like properties of BL films, made with bis(ethylenedithio)-tetrathiafulvalene (BEDT-TTF or ET) α-ET2I3 (nano)microcrystals as two-dimensional molecular conductor on one side and insulator polycarbonate as a second ingredient, are attributed to an emergent phenomenon equivalent to the transition from an insulator to a metal. This made it possible to obtain semiconducting BL films with tunable electrical/optical bandgaps ranging from 0 to 2.9 eV. A remarkable aspect is the similarity close to room temperature of the thermal and mechanical properties of both composite components, making these materials ideal candidates to fabricate flexible and soft sensors for stress, pressure, and temperature aiming at applications in wearable human health care and bioelectronics.

Machine learned calibrations to high-throughput molecular excited state calculations

Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions), so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique [eXtended Tight Binding based simplified Tamm-Dancoff approximation (xTB-sTDA)] against a higher accuracy one (time-dependent density functional theory). Testing the calibration model shows an approximately sixfold decrease in the error in-domain and an approximately threefold decrease in the out-of-domain. The resulting mean absolute error of ∼0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates that machine learning can be used to develop a cost-effective and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.

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.

Active discovery of organic semiconductors

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.