The role of crystal packing on the optical response of trialkyltetrelethynyl acenes.

The electronic and optical responses of an organic semiconductor (OSC) are dictated by the chemistries of the molecular or polymer building blocks and how these chromophores pack in the solid state. Understanding the physicochemical nature of these responses is not only critical for determining the OSC performance for a particular application, but the UV/visible optical response may also be of potential use to determine aspects of the molecular-scale solid-state packing for crystal polymorphs or thin-film morphologies that are difficult to determine otherwise. To probe these relationships, we report the quantum-chemical investigation of a series of trialkyltetrelethynyl acenes (tetrel = silicon or germanium) that adopt the brickwork, slip-stack, or herringbone (HB) packing configurations; the π-conjugated backbones considered here are pentacene and anthradithiophene. For comparison, HB-packed (unsubstituted) pentacene is also included. Density functional theory and G0W0 (single-shot Green's function G and/or screened Coulomb function W) electronic band structures, G0W0-Bethe-Salpeter equation-derived optical spectra, polarized ϵ2 spectra, and distributions of both singlet and triplet exciton wave functions are reported. Configurational disorder is also considered. Furthermore, we evaluate the probability of singlet fission in these materials through energy conservation relationships.

Predicting inorganic dimensionality in templated metal oxides.

Amine-templated metal oxides are a class of hybrid organic-inorganic compounds with great structural diversity; by varying the compositions, 0D, 1D, 2D, and 3D inorganic dimensionalities can be achieved. In this work, we created a dataset of 3725 amine-templated metal oxides (including some metalloid oxides), their composition, amine identity, and dimensionality, extracted from the Cambridge Structure Database (CSD), which spans 71 elements, 25 main group building units, and 349 amines. We characterize the diversity of this dataset over reactants and in time. Artificial neural network models trained on this dataset can predict the most and least probable outcome dimensionalities with 71% and 95% accuracies, respectively, using only information about reactant identities, without stoichiometric information. Surprisingly, the amine identity plays only a minor role in most cases, as omitting this information only reduces the accuracy by <2%. The generality of this model is demonstrated on a time held-out test set of 36 amine-templated lanthanide oxalates, vanadium tellurites, vanadium selenites, vanadates, molybdates, and molybdenum sulfates, whose syntheses and structural characterizations are reported here for the first time, and which contain two new element combinations and four amines that are not present in the CSD.

OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors.

Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu.

Exploiting Excited-State Aromaticity To Design Highly Stable Singlet Fission Materials.

Singlet fission, the process of forming two triplet excitons from one singlet exciton, is a characteristic reserved for only a handful of organic molecules due to the atypical energetic requirement for low energy excited triplet states. The predominant strategy for achieving such a trait is by increasing ground state diradical character; however, this greatly reduces ambient stability. Herein, we exploit Baird's rule of excited state aromaticity to manipulate the singlet-triplet energy gap and create novel singlet fission candidates. We achieve this through the inclusion of a [4n] 5-membered heterocycle, whose electronic resonance promotes aromaticity in the triplet state, stabilizing its energy relative to the singlet excited state. Using this theory, we design a family of derivatives of indolonaphthyridine thiophene (INDT) with highly tunable excited state energies. Not only do we access novel singlet fission materials, they also exhibit excellent ambient stability, imparted due to the delocalized nature of the triplet excited state. Spin-coated films retained up to 85% activity after several weeks of exposure to oxygen and light, while analogous films of TIPS-pentacene showed full degradation after 4 days, showcasing the excellent stability of this class of singlet fission scaffold. Extension of our theoretical analysis to almost ten thousand candidates reveals an unprecedented degree of tunability and several thousand potential fission-capable candidates, while clearly demonstrating the relationship between triplet aromaticity and singlet-triplet energy gap, confirming this novel strategy for manipulating the exchange energy in organic materials.

Extracting structured data from organic synthesis procedures using a fine-tuned large language model.

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD "messages" (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.

Active Learning of Ligands That Enhance Perovskite Nanocrystal Luminescence.

Ligands play a critical role in the optical properties and chemical stability of colloidal nanocrystals (NCs), but identifying ligands that can enhance NC properties is daunting, given the high dimensionality of chemical space. Here, we use machine learning (ML) and robotic screening to accelerate the discovery of ligands that enhance the photoluminescence quantum yield (PLQY) of CsPbBr3 perovskite NCs. We developed a ML model designed to predict the relative PL enhancement of perovskite NCs when coordinated with a ligand selected from a pool of 29,904 candidate molecules. Ligand candidates were selected using an active learning (AL) approach that accounted for uncertainty quantified by twin regressors. After eight experimental iterations of batch AL (corresponding to 21 initial and 72 model-recommended ligands), the uncertainty of the model decreased, demonstrating an increased confidence in the model predictions. Feature importance and counterfactual analyses of model predictions illustrate the potential use of ligand field strength in designing PL-enhancing ligands. Our versatile AL framework can be readily adapted to screen the effect of ligands on a wide range of colloidal nanomaterials.

Multiexciton quintet state populations in a rigid pyrene-bridged parallel tetracene dimer.

The multiexciton quintet state, 5TT, generated as a singlet fission intermediate in pairs of molecular chromophores, is a promising candidate as a qubit or qudit in future quantum information science schemes. In this work, we synthesize a pyrene-bridged parallel tetracene dimer, TPT, with an optimized interchromophore coupling strength to prevent the dissociation of 5TT to two decorrelated triplet (T1) states, which would contaminate the spin-state mixture. Long-lived and strongly spin-polarized pure 5TT state population is observed via transient absorption spectroscopy and transient/pulsed electron paramagnetic resonance spectroscopy, and its lifetime is estimated to be >35 µs, with the dephasing time (T2) for the 5TT-based qubit measured to be 726 ns at 10 K. Direct relaxation from 1TT to the ground state does diminish the overall excited state population, but the exclusive 5TT population at large enough persistent density for pulsed echo determination of spin coherence time is consistent with recent theoretical models that predict such behavior for strict parallel chromophore alignment and large exchange coupling.

Thin-Film Organic Heteroepitaxy.

Incorporating crystalline organic semiconductors into electronic devices requires understanding of heteroepitaxy given the ubiquity of heterojunctions in these devices. However, while rules for commensurate epitaxy of covalent or ionic inorganic material systems are known to be dictated by lattice matching constraints, rules for heteroepitaxy of molecular systems are still being written. Here, it is found that lattice matching alone is insufficient to achieve heteroepitaxy in molecular systems, owing to weak intermolecular forces that describe molecular crystals. It is found that, in addition, the lattice matched plane also must be the lowest energy surface of the adcrystal to achieve one-to-one commensurate molecular heteroepitaxy over a large area. Ultraviolet photoelectron spectroscopy demonstrates the lattice matched interface to be of higher electronic quality than a disordered interface of the same materials.

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

SELFIES and the future of molecular string representations.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

Suppressing bias stress degradation in high performance solution processed organic transistors operating in air.

Solution processed organic field effect transistors can become ubiquitous in flexible optoelectronics. While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications. Here, we introduce a strategy to identify the most probable and severe degradation pathways in organic transistors and then implement a method to eliminate the main sources of instabilities. Real time monitoring of the energetic distribution and transformation of electronic trap states during device operation, in conjunction with simulations, revealed the nature of traps responsible for performance degradation. With this information, we designed the most efficient encapsulation strategy for each device type, which resulted in fabrication of high performance, environmentally and operationally stable small molecule and polymeric transistors with consistent mobility and unparalleled threshold voltage shifts as low as 0.1 V under the application of high bias stress in air.

An unusually short inter-molecular N-H⋯N hydrogen bond in crystals of the hemi-hydro-chloride salt of 1-exo-acetamido-pyrrolizidine.

The title compound [systematic name: (1R*, 8S)-2-acetamidoocta-hydro-pyrrol-izin-4-ium chloride-N-[(1R, 8S)-hexa-hydro-1H-pyrrolizin-2-yl)acetamide (1/1)], 2(C9H16N2O)·HCl or C9H17N2O+·Cl-·C9H16N2O, arose as an unexpected product when 1-exo-acetamido-pyrrolizidine (AcAP; C9H16N2O) was dissolved in CHCl3. Within the AcAP pyrrolizidine group, the unsubstituted five-membered ring is disordered over two orientations in a 0.897 (5):0.103 (5) ratio. Two AcAP mol-ecules related by a crystallographic twofold axis link to H+ and Cl- ions lying on the rotation axis, thereby forming N-H⋯N and N-H⋯Cl⋯H-N hydrogen bonds. The first of these has an unusually short N⋯N separation of 2.616 (2) Å: refinement of different models against the present data set could not distinguish between a symmetrical hydrogen bond (H atom lying on the twofold axis and equidistant from the N atoms) or static or dynamic disorder models (i.e. N-H⋯N + N⋯H-N). Computational studies suggest that the disorder model is slightly more stable, but the energy difference is very small.

Computationally aided design of a high-performance organic semiconductor: the development of a universal crystal engineering core.

Herein, we describe the design and synthesis of a suite of molecules based on a benzodithiophene "universal crystal engineering core". After computationally screening derivatives, a trialkylsilylethyne-based crystal engineering strategy was employed to tailor the crystal packing for use as the active material in an organic field-effect transistor. Electronic structure calculations were undertaken to reveal derivatives that exhibit exceptional potential for high-efficiency hole transport. The promising theoretical properties are reflected in the preliminary device results, with the computationally optimized material showing simple solution processing, enhanced stability, and a maximum hole mobility of 1.6 cm2 V-1 s-1.

Unusual Electronic Structure of the Donor-Acceptor Cocrystal Formed by Dithieno[3,2-a:2',3'-c]phenazine and 7,7,8,8-Tetracyanoquinodimethane.

Mixed cocrystals derived from electron-rich donor (D) and electron-deficient acceptor (A) molecules showcase electronic, optical, and magnetic properties of interest for a wide range of applications. We explore the structural and electronic properties of a cocrystal synthesized from dithieno[3,2-a:2',3'-c]phenazine (DTPhz) and 7,7,8,8-tetracyanoquinodimethane (TCNQ), which has a mixed-stack packing arrangement of the (π-electronic) face-to-face stacks in a 2:1 D:A stoichiometry. Density functional theory investigations reveal that the primary electronic characteristics of the cocrystal are not determined by electronic interactions along the face-to-face stacks, but rather they are characterized by stronger electronic interactions orthogonal to these stacks that follow the edge-to-edge donor-donor or acceptor-acceptor contacts. These distinctive electronic characteristics portend semiconducting properties that are unusual for semiconducting mixed cocrystals and suggest further potential to design organic semiconductors with orthogonal transport characteristics for different charge carriers.