Using graphs to quantify energetic and structural order in semicrystalline oligothiophene thin films

Van, Ellen; Jones, Matthew L.; Jankowski, Eric; Wodo, Olga

doi:10.1039/c8me00028j

Cited by 14 publications

(19 citation statements)

References 65 publications

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“…Time‐of‐flight measurements of hole mobility in P3HT experiments range from μ = 1 × 10 −5 to 1 × 10 −3 cm 2 /Vs . Computational work has helped to explain the role of thiophene ring orientation on charge transport, and kinetic Monte Carlo (KMC) simulations of charge transport have predicted mobilities ranging from μ = 1 × 10 −4 to 0.6 cm 2 /Vs, depending on the degree of ordering in of the P3HT morphologies. These experimental and computational predictions of mobility provide references for validation: Calculated hole mobilities in P3HT should fall between μ = 1 × 10 −4 to 0.6 cm 2 /Vs and increase with increasing P3HT crystallinity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

et al. 2019

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“…have studied the evaporation‐induced phase separation based on the Cahn–Hilliard–Cook equation . Based on these precise morphologies, dynamic Monte Carlo or graph theory–based formulations are used to characterize the electrical properties.…”

Section: Introductionmentioning

confidence: 99%

Impact of Phosphorescent Sensitizers and Morphology on the Photovoltaic Performance in Organic Solar Cells

Popp

Kaiser

Gagliardi

2018

Advcd Theory and Sims

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Phosphorescent sensitizers (PSs) are considered as a promising alternative for increasing the internal quantum efficiency (IQE) in organic solar cells (OSCs). By converting short-lifetime singlet into long-living triplet excitons, enhanced exciton diffusion and dissociation have been reported previously. However, only a limited increase in the OSC performance has been achieved. In this work, the interplay of the PS with both singlet and triplet excitons within organic blends is examined using kinetic Monte Carlo simulations including a comprehensive model of excitonic processes. Different morphologies of the conventional P3HT:PCBM solar cell are simulated, and the excitonic properties and their influence on the photovoltaic performance under doping are studied. The use of phosphorescent sensitization ensures high intersystem crossing and enlarges the diffusion length. An increase in the IQE of 34% is observed for a bilayer OSC. The increasing decay of triplets in proximity to the PS due to a strong spin-orbit coupling limits the IQE. Unlike expected, triplet-triplet annihilation does not provide a significant loss of excitons. A doped planar-mixed molecular heterojunction outperforms an undoped bulk-heterojunction OSC due to the enhanced exciton diffusion. A further study of optimal PS parameters predicts an increase in the IQE within bilayer solar cells by about 100%.

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“…This term originates from Mott's variable range hopping theory (VRH) [42], which is often used in polymer hopping theory [43,44]. VRH accounts for deficiencies in the prediction of transfer integrals within amorphous systems using the above method, which do not sufficiently suppress the electronic coupling between chromophores with large separations, leading to unphysical carrier motion [31]. The reorganization energy, λ, is the energy required to polarize and depolarize a chromophore, in response to a carrier hopping from one to another.…”

Section: Kinetic Monte Carlo Simulationsmentioning

confidence: 99%

“…Conversely, master equation and "molecular" KMC maintain the molecular resolution but require approximations such as periodic boundary conditions to investigate charge motion over distances relevant for devices [25,28,29]. Such methods have been used to investigate time-of-flight mobilities, some reporting values a few orders of magnitude higher than expected (1× 10 0 to 1× 10 3 cm 2 /Vs) [28,31,32], and others focusing on transfer integrals and inferred mobility without predicting mobility values [26,33]. For this investigation, we implement molecular KMC simulations, which are Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 19 November 2018 doi:10.20944/preprints201811.0427.v1 more computationally expensive than master equation techniques, but offer explicit spatial resolution of charges within the morphology [28,29].…”

Section: Introductionmentioning

confidence: 99%

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains

Miller¹,

Jones²,

Jankowski³

2018

Preprint

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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.

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Using graphs to quantify energetic and structural order in semicrystalline oligothiophene thin films

Abstract: In semicrystalline conjugated polymer thin films, the mobility of charges depends on the arrangement of the individual polymer chains.

Cited by 14 publications

References 65 publications

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

Impact of Phosphorescent Sensitizers and Morphology on the Photovoltaic Performance in Organic Solar Cells

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains

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