Quantitative Structure-Property Relationships for the Electronic Properties of Polycyclic Aromatic Hydrocarbons

Size Isn't Everything: Geometric Tuning in Polycyclic Aromatic Hydrocarbons and Its Implications for Carbon Nanodots

Recent developments in light-emitting carbon nanodots and molecular organic semiconductors have seen renewed interest in the properties of polycyclic aromatic hydrocarbons (PAHs) as a family. The networks of delocalized π electrons in sp2-hybridized carbon grant PAHs light-emissive properties right across the visible spectrum. However, the mechanistic understanding of their emission energy has been limited due to the ground state-focused methods of determination. This computational chemistry work, therefore, seeks to validate existing rules and elucidate new features and characteristics of PAHs that influence their emissions. Predictions based on (time-dependent) density functional theory account for the full 3-dimensional electronic structure of ground and excited states and reveal that twisting and near-degeneracies strongly influence emission spectra and may therefore be used to tune the color of PAHs and, hence, carbon nanodots. We particularly note that the influence of twisting goes beyond torsional destabilization of the ground-state and geometric relaxation of the excited state, with a third contribution associated with the electric transition dipole. Symmetries and peri-condensation may also have an effect, but this could not be statistically confirmed. In pursuing this goal, we demonstrate that with minimal changes to molecular size, the entire visible spectrum may be spanned by geometric modification alone; we have also provided a first estimate of emission energy for 35 molecules currently lacking published emission spectra as well as clear guidelines for when more sophisticated computational techniques are required to predict the properties of PAHs accurately.

Substitution Effect of a Single Nitrogen Atom on π-Electronic Systems of Linear Polycyclic Aromatic Hydrocarbons (PAHs): Theoretically Visualized Coexistence of Mono- and Polycyclic π-Electron Delocalization

We theoretically investigated the nitrogen substitution effect on the molecular structure and π-electron delocalization in linear nitrogen-substituted polycyclic aromatic hydrocarbons (N-PAHs). Based on the optimized molecular structures and magnetic field-induced parameters of fused bi- and tricyclic linear N-PAHs, we found that the local π-electron delocalization of subcycles (e.g., mono- and bicyclic constituent moieties) in linear N-PAHs is preserved, despite deviation from ideal structures of parent monocycles. The introduction of a fused five-membered ring with a pyrrolic N atom (N-5MR) in linear N-PAHs significantly perturbs the π-electronic condition of the neighboring fused six-membered ring (6MR). Monocyclic pyrrole exhibits substantial bond length alternations, strongly influencing the π-electronic systems of both the fused N-5MR and 6MR in linear N-PAHs, depending on the location of shared covalent bonds. A fused six-membered ring with a graphitic N atom in an indolizine moiety cannot generate monocyclic π-electron delocalization but instead contributes to the formation of polycyclic π-electron delocalization. This is evidenced by bifurcated diatropic ring currents induced by an external magnetic field. In conclusion, the satisfaction of Hückel's 4n + 2 rule for both mono- and polycycles is crucial for understanding the overall π-electron delocalization. It is crucial to consider the unique characteristics of the three types of substituted N atoms and the spatial arrangement of 5MR and 6MR in N-PAHs.

Atom-Based Machine Learning Model for Quantitative Property-Structure Relationship of Electronic Properties of Fusenes and Substituted Fusenes

This study presents the development of machine-learning-based quantitative structure-property relationship (QSPR) models for predicting electron affinity, ionization potential, and band gap of fusenes from different chemical classes. Three variants of the atom-based Weisfeiler-Lehman (WL) graph kernel method and the machine learning model Gaussian process regressor (GPR) were used. The data pool comprises polycyclic aromatic hydrocarbons (PAHs), thienoacenes, cyano-substituted PAHs, and nitro-substituted PAHs computed with density functional theory (DFT) at the B3LYP-D3/6-31+G(d) level of theory. The results demonstrate that the GPR/WL kernel methods can accurately predict the electronic properties of PAHs and their derivatives with root-mean-square deviations of 0.15 eV. Additionally, we also demonstrate the effectiveness of the active learning protocol for the GPR/WL kernel methods pipeline, particularly for data sets with greater diversity. The interpretation of the model for contributions of individual atoms to the predicted electronic properties provides reasons for the success of our previous degree of π-orbital overlap model.

Machine Learning-Based Quantitative Structure-Property Relationships for the Electronic Properties of Cyano Polycyclic Aromatic Hydrocarbons

In this study, quantitative structure-property relationships (QSPR) based on a machine learning (ML) methodology and the truncated degree of π-orbital overlap (DPO) to predict the electronic properties, namely, the bandgaps, electron affinities, and ionization potentials of the cyano polycyclic aromatic hydrocarbon (CN-PAH) chemical class were developed. The level of theory B3LYP/6-31+G(d) of density functional theory (DFT) was used to calculate a total of 926 data points for the development of the QSPR model. To include the substituents effects, a new descriptor was added to the DPO model. Consequently, the new ML-DPO model yields excellent linear correlations to predict the desired electronic properties with high accuracy to within 0.2 eV for all multi-CN-substituted PAHs and 0.1 eV for the mono-CN-substituted PAH subclass.

Application of Machine Learning in Developing Quantitative Structure-Property Relationship for Electronic Properties of Polyaromatic Compounds

The degree of π orbital overlap (DPO) model has been demonstrated to be an excellent quantitative structure-property relationship (QSPR) that can map two-dimensional structural information of polycyclic aromatic hydrocarbons (PAHs) and thienoacenes to their electronic properties, namely, band gaps, electron affinities, and ionization potentials. However, the model suffers from significant limitations that narrow its applications due to inefficient manual procedures in parameter optimization and descriptor formulation. In this work, we developed a machine learning (ML)-based method for efficiently optimizing DPO parameters and proposed a truncated DPO descriptor, which is simple enough that can be automatically extracted from simplified molecular-input line-entry system strings of PAHs and thienoacenes. Compared with the result from our previous studies, the ML-based methodology can optimize DPO parameters with four times fewer data, while it can achieve the same level of accuracy in predictions of the mentioned electronic properties to within 0.1 eV. The truncated DPO model also has similar accuracy to the full DPO model. Consequently, the ML-based DPO approach coupled with the truncated DPO model enables new possibilities for developing automatic pipelines for high-throughput screening and investigating new QSPR for new chemical classes.

Assembling photoactive materials from polycyclic aromatic hydrocarbons (PAHs): room temperature phosphorescence and excimer-emission in co-crystals with 1,4-diiodotetrafluorobenzene

Co-crystallization of PAHs with a polyhalogenated co-former afforded three novel co-crystals, which display remarkable features such as mechanochemical interconversion, photoreactivity, excimer fluorescence, and RTP phosphorescence in the solid state.

A Computational Study of the Electronic Properties of Heterocirculenes: Oxiflowers and Sulflowers

This study investigated the relationship of electronic properties with some structural parameters of two circulene classes: Sulflowers and Oxiflowers. It is found that correlations between the HOMO-LUMO gap and some electronic properties of these circulenes are opposite to those of linear conjugated structures. Moreover, a new hybrid molecule, called an Oxisulflower, is proposed to be a potential structure for synthesizing as Sulflower. Also, a brand-new descriptor, namely, the "degree of non-planarity", is evaluated with excellent correlations with the HOMO-LUMO gap of molecules in Oxiflower and Sulflower classes. The correlations have also shown that the steric characteristic of a structure can be controlled to modulate its band gap for studying the prediction science of the electronic properties in developing organic semiconductors.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure-property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

Pd(II)-Catalyzed One-Pot Multiple C-C Bond Formation: En Route Synthesis of Succinimide-Fused Unsymmetrical 9,10-Dihydrophenanthrenes from Aryl Iodides and Maleimides

An expeditious approach has been developed for the synthesis of succinimide-fused unsymmetrical 9,10-dihydrophenanthrenes from simple aryl iodides and maleimides. The developed transformation, overall proceeding with high regioselectivity via a cascade approach through palladium(II)-catalyzed Micheal-type addition/C-H activation/intramolecular cross-dehydrogenative coupling (ICDC)/C-H activation, allows formation of four fundamental carbon-carbon bonds in one-pot fashion. The reactions tolerate broad functional groups and satisfy the parameters of atom and step economy. Detailed mechanistic studies were carried out to support the proposed synthetic pathway.

Quantum Mechanical-Based Quantitative Structure-Property Relationships for Electronic Properties of Two Large Classes of Organic Semiconductor Materials: Polycyclic Aromatic Hydrocarbons and Thienoacenes

In this study, the degree of the π-orbital overlap (DPO) model proposed earlier for polycyclic aromatic hydrocarbons (PAH) was employed to develop quantitative structure-property relationships (QSPRs) for band gaps, ionization potentials, and electron affinities of thienoacenes. DPO is based on two-dimensional topological draw of aromatic molecules. The B3LYP/6-31+G(d) level of density functional theory (DFT) was used to provide chemical data for developing QSPRs. We found that the DPO model is able to capture the correct physics of electronic properties of aromatic molecules so that with only six nonzero topological parameters (four for PAH and additional two for thienoacenes), the DPO model yields the linear dependence of electronic properties of both the PAH and thienoacenes classes by a single set of QSPRs with the accuracy to within 0.1 eV of the DFT results. The results suggest that within the DPO framework, all aromatic molecules can share the same set of QSPRs.

A quantitative structure-property study of reorganization energy for known p-type organic semiconductors

Intramolecular reorganization energy (RE), which quantifies the electron-phonon coupling strength, is an important charge transport parameter for the theoretical characterization of molecular organic semiconductors (OSCs). On a small scale, the accurate calculation of the RE is trivial; however, for large-scale screening, faster approaches are desirable. We investigate the structure-property relations and present a quantitative structure-property relationship study to facilitate the computation of RE from molecular structure. To this end, we generated a compound set of 171, which was derived from known p-type OSCs built from moieties such as acenes, thiophenes, and pentalenes. We show that simple structural descriptors such as the number of atoms, rings or rotatable bonds only weakly correlate with the RE. On the other hand, we show that regression models based on a more comprehensive representation of the molecules such as SMILES-based molecular signatures and geometry-based molecular transforms can predict the RE with a coefficient of determination of 0.7 and a mean absolute error of 40 meV in the library, in which the RE ranges from 76 to 480 meV. Our analysis indicates that a more extensive compound set for training is necessary for more predictive models.