RESUMEN
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.
RESUMEN
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.
RESUMEN
Exploiting the capabilities of organic semiconductors for applications ranging from light-emitting diodes to photovoltaics to lasers relies on the creation of ordered, smooth layers for optimal charge carrier mobilities and exciton diffusion. This, in turn, creates a demand for organic small molecules that can form smooth thin film crystals via homoepitaxy. We have studied a set of small-molecule organic semiconductors that serve as templates for homoepitaxy. The surface roughness of these materials is measured as a function of adlayer film thickness from which the growth exponent (ß) is extracted. Notably, we find that three-dimensional molecules that have low molecular aspect ratios (AR) tend to remain smooth as thickness increases (small ß). This is in contrast to planar or rod-like molecules with high AR that quickly roughen (large ß). Molecular dynamics simulations find that the Ehrlich-Schwöbel barrier (EES) alone is unable to fully explain this trend. We further investigated the mobility of ad-molecules on the crystalline surface to categorize their diffusion behaviors and the effects of aggregation to account for the different degrees of roughness that we observed. Our results suggest that low AR molecules have low molecular mobility and moderate EES which creates a downward funneling effect leading to smooth crystal growth.
RESUMEN
Doping has proven to be a critical tool for enhancing the performance of organic semiconductors in devices like organic light-emitting diodes. However, the challenge in working with high-ionization-energy (IE) organic semiconductors is to find p-dopants with correspondingly high electron affinity (EA) that will improve the conductivity and charge carrier transport in a film. Here, we use an oxidant that has been recently recognized to be a very strong p-type dopant, hexacyano-1,2,3-trimethylene-cyclopropane (CN6-CP). The EA of CN6-CP has been previously estimated via cyclic voltammetry to be 5.87 eV, almost 300 meV higher than other known high-EA organic molecular oxidants. We measure the frontier orbitals of CN6-CP using ultraviolet and inverse photoemission spectroscopy techniques and confirm a high EA value of 5.88 eV in the condensed phase. The introduction of CN6-CP in a film of large-band-gap, large-IE phenyldi(pyren-1-yl)phosphine oxide (POPy2) leads to a significant shift of the Fermi level toward the highest occupied molecular orbital and a 2 orders of magnitude increase in conductivity. Using CN6-CP and n-dopant (pentamethylcyclopentadienyl)(1,3,5-trimethylbenzene)ruthenium (RuCp*Mes)2, we fabricate a POPy2-based rectifying p-i-n homojunction diode with a 2.9 V built-in potential. Blue light emission is achieved under forward bias. This effect demonstrates the dopant-enabled hole injection from the CN6-CP-doped layer and electron injection from the (RuCp*Mes)2-doped layer in the diode.
RESUMEN
Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal-organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy-efficiency tradeoffs have not been examined in depth. In this study, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.