TOWARDS THE RATIONAL DESIGN OF ORGANIC SEMICONDUCTORS THROUGH COMPUTATIONAL APPROACHES

Though organic semiconductors have illustrated potential as industry-relevant materials for electronics applications, there are few guidelines that can take one from molecular design to functional materials. This limitation is, in part, due to incomplete understanding as to how the atomic-scale construction of the π-conjugated molecules that comprise the organic semiconductors determines the nature and strength of both the noncovalent intramolecular interactions that govern molecular conformation and noncovalent intermolecular interactions that regulate the energetic preference for solid-state packing. Hence, there remain several fundamental questions that need to be resolved in order to design organic semiconductors from a priori knowledge, including: What is the relevance of the relatively weak noncovalent intramolecular interactions on determining molecular structure, are current hypotheses put forward as to important interactions valid, and how does chemical substitution as various positions along the π-conjugated backbone impact these interactions? How do the intermolecular noncovalent interactions regulate solid-state packing, are there features of the molecular structure – e.g. the π-conjugated backbone, heteroatoms, or pendent alkyl chains – that play a more important role? What connections can be made between the structures/properties of the π-conjugated molecules and the resulting organic semiconductors? In this dissertation, Chapter 1 provides an introductory discussion of these questions and a brief review of previous studies. Chapter 2 details the computational approaches that were implemented throughout the course of the thesis work. Chapter 3 describes the investigation of a series of pyrene-acene molecules to illustrate the importance of choosing the right molecular structure in π-conjugated chromophores. In Chapter 4, S...F noncovalent intramolecular interactions are systematically investigated in two separate cases to highlight the varied impact that these interactions can have on molecular and solid-state packing structures. Chapter 5 describes the investigation of an oscillatory crystal packing structure observed for a series of oligothiophenes that follow the odd-even carbon-atom counts of the pendant alkyl chains. In Chapter 6, the polymorphism of functionalized pentacene molecules is studied to reveal how seemingly simple atomic substitutions can drastically alter solid-state packing. To systematically address the aforementioned fundamental questions, Chapter 7 describes the construction and application of a database of crystalline molecular organic semiconductors. Finally, perspectives regarding future research are provided in Chapter 8

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

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

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

Non-covalent close contacts in fluorinated thiophene-phenylene-thiophene conjugated units: understanding the nature and dominance of O···H versus S···F and O···F interactions towards the control of polymer conformation

Using a simple -conjugated trimer, EDOT-phenylene-EDOT (where EDOT = 3,4-ethylenedioxythiophene), we evaluate the effect that fluorine substituents have upon changes in conformation, conjugation and oxidation potentials in -conjugated structures. These variations are assessed as a function of the fluorine atom’s propensity to feature in hydrogen and/or halogen bonding with other heteroatoms. The molecular motif was chosen because the EDOT unit presents the possibility of competing O···X or S···X non-covalent contacts (where X = H or F). Such non-bonding interactions are acknowledged to be highly influential in dictating molecular and polymer morphology and inducing changes in certain physical properties. We have studied four compounds, beginning with an unsubstituted bridging phenylene ring and then adding one, two or four fluorine units to the parent molecule. Our studies involve single crystal XRD studies, cyclic voltammetry, absorption spectroscopy and density functional theory calculations to identify the dominant non-covalent interactions and elucidate their effects on the molecules described. Experimental studies have also been carried out on the corresponding electrochemically synthesized polymers to confirm that these non-covalent interactions and their effects persist in polymers. Our findings show that hydrogen bonding and halogen bonding feature in these molecules and their corresponding polymers. ABSTRACT: Using a simple -conjugated trimer, EDOT-phenylene-EDOT (where EDOT = 3,4-ethylenedioxythiophene), we evaluate the effect that fluorine substituents have upon changes in conformation, conjugation and oxidation potentials in -conjugated structures. These variations are assessed as a function of the fluorine atom’s propensity to feature in hydrogen and/or halogen bonding with other heteroatoms. The molecular motif was chosen because the EDOT unit presents the possibility of competing O···X or S···X non-covalent contacts (where X = H or F). Such non-bonding interactions are acknowledged to be highly influential in dictating molecular and polymer morphology and inducing changes in certain physical properties. We have studied four compounds, beginning with an unsubstituted bridging phenylene ring and then adding one, two or four fluorine units to the parent molecule. Our studies involve single crystal XRD studies, cyclic voltammetry, absorption spectroscopy and density functional theory calculations to identify the dominant non-covalent interactions and elucidate their effects on the molecules described. Experimental studies have also been carried out on the corresponding electrochemically synthesized polymers to confirm that these non-covalent interactions and their effects persist in polymers. Our findings show that hydrogen bonding and halogen bonding feature in these molecules and their corresponding polymers

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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

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: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, 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.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

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

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. 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

Environmental Stability of Crystals: Why Be Greedy When You Can Be Exact?

In a recent paper in this journal (Chem. Mater. 2022, 34, 2545-2552), Twyman et al. studied the environmental stability of crystals by introducing a greedy heuristic algorithm for determining possible oxidation reactions. We show how the problem can be solved exactly, with less code and comparable computational time by reformulating it as a linear optimization for the reaction enthalpy. The enthalpy of oxidation and the oxidation products, computed by Twyman et al.’s greedy heuristic is suboptimal in 22504 of 39634 cases (56.8%), with the error in enthalpy of oxidation as large as 1.480 eV/atom. Every one of these quantitative errors also results in qualitative differences in the oxide species formed. Using our exact approach to the problem, we re-evaluate the core results from the paper by Twyman et al. We also describe a minor modification enabling calculations of oxidation free energy minima