Due to its direct band gap and light mass, the recently synthesized triazine-based, graphitic carbon nitride (TGCN) is considered a promising material for future microelectronics. However, despite the structural similarity with completely planar carbon-only graphene, TGCN sheets are different because of the presence of buckling distortions making the TGCN sheets nonplanar. In this article, we show that the sufficiently strong coupling between the unoccupied molecular orbitals (UMOs) with occupied molecular orbitals (OMOs) leads to pseudo Jahn–Teller distortions (PJT) and consequent buckling of TGCN layers. Doping the TGCN with doubly charged cations such as Be2+ can suppress the PJT distortions resulting in a completely planar structure. A proper understanding of the mechanism of the PJT effect in TGCN is crucial for tailoring properties that are relevant for practical applications.

Molecular simulations have the potential to advance the understanding of how the structure of organic materials can be engineered through the choice of chemical components but are limited by computational costs. The computational costs can be significantly lowered through the use of modeling approximations that capture the relevant features of a system, while lowering algorithmic complexity or by decreasing the degrees of freedom that must be integrated. Such methods include coarse-graining techniques, approximating long-range electrostatics with short-range potentials, and the use of rigid bodies to replace flexible bonded constraints between atoms. To understand whether and to what degree these techniques can be leveraged to enhance the understanding of planar organic molecules, we investigate the morphologies predicted by molecular dynamic simulations using simplified molecular models of perylene and perylothiophene. Approximately, 10 000 wall-clock hours of graphics processing unit-accelerated simulations are performed using both rigid and flexible models to test their efficiency and predictive capability with the two chemistries. We characterize the 1191 resulting morphologies using simulated X-ray diffraction and cluster analysis to distinguish structural transitions, summarized by four phase diagrams. We find that the morphologies generated by the rigid model of perylene and perylothiophene match with those generated by the flexible model. We find that ordered, hexagonally packed columnar phases are thermodynamically favored over a wide range of densities and temperatures for both molecules, in qualitative agreement with experiments. Furthermore, we find the rigid model to be more computationally efficient for both molecules, providing more samples per second and shorter times to equilibrium. Owing to the structural accuracy and improved computational efficiency of modeling polyaromatic groups as rigid bodies, we recommend this modeling choice for enhancing the sampling in polyaromatic molecular simulations.

Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ∼100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role “tie-chains” play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.

The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly‐(3‐hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information‐dense, and easy‐to‐implement approach for this problem, achieving mean‐absolute‐error of 0.02 [× 1.6 × 10−19 J], R2 = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero‐field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability.

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.

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We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼ 350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.

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