Computationally connecting organic photovoltaic performance to atomistic arrangements and bulk morphology

Jones, Matthew L.; Jankowski, Eric

doi:10.1080/08927022.2017.1296958

Cited by 26 publications

(58 citation statements)

References 149 publications

(179 reference statements)

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“…To characterize the molecular packings obtained in our simulations we use two structural metrics: an order parameter and simulated grazing incident X-ray scattering (GIXS) using the Diffractometer simulation software [35,69]. GIXS patterns are used to identify and quantify periodic morphological features and are used to validate predicted structures directly against experiments.…”

Section: Morphology Characterizationmentioning

confidence: 99%

“…At the largest scales, the structural evolution of 5 million coarsely-modeled P3HT monomers can be accessed on > 100 nm length-scales [34], but the computational cost of evaluating each step meant that equilibration was inaccessible over the 400 ns simulation trajectory. At 11-nm scales, equilibration of coarse-grained P3HT models are achievable over ∼2 µs simulation trajectories [28], but such coarse models miss the π-stacking details of P3HT rings, which can have implications for charge transport calculations [35,36]. Long relaxation times can be avoided in MD simulations through carefully selected initial conditions [32,[37][38][39][40], but these simulations can only check if a structure is locally stable, not whether it will robustly self-assemble at a particular state-point.…”

Section: Introductionmentioning

confidence: 99%

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Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

Miller¹,

Jones²,

Henry³

et al. 2018

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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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Section: Morphology Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

Miller¹,

Jones²,

Henry³

et al. 2018

Preprint

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“…46 where the scattered intensity I at wave vector ⃗ q is related to the structure factor and the form factor P (⃗ q) as:…”

Section: Characterising Structure From Molecuar Dynamics (Md) Simulatmentioning

confidence: 99%

“…In [40]: from cme_utils.job_control import signac_helpers as diff diff_keys = diff.diff_jobs(data_path,'9e8bffd84a3fa9c262969f00552f4d08','4f8c0106906f219 e44fa45cd734a989c') In [46]: …”

Section: (Df['t']>200)and #Because Except One Of the Tajectories Out Ofmentioning

confidence: 99%

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New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics

Thomas¹

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Graduate College. dedicated to curiosity, tenacity and our families. iv ACKNOWLEDGMENTS I would like to thank my advisor Dr. Eric Jankowski for giving me the golden opportunity to work with him and setting me up on a path to success from day one. I want to especially thank him for showing me by example, how to be a good scientist, communicator and a strategist. I am grateful to Dr. Carla Reynolds for guiding me through this project and helping me get the practical perspective on ideas that I came up with. I am also thankful for her for helping me run many of the simulations on the Garcia cluster computer at Boeing and being the first beta tester for epoxpy. I also want to thanks our "American" cousins Sanju, Reshma, Vinu, Megha and our "American" aunt and uncle, Geetha and Dr. Mathew for being our Santa through our college life. ABSTRACTThis research aims at developing new computational methods to understand the molecular self-assembly of reacting systems whose complex structures depend on the thermodynamics of mixing, reaction kinetics, and diffusion kinetics. The specific reacting system examined in this study is epoxy, cured with linear chain thermoplastic tougheners whose complex microstructure is known from experiments to affect mechanical properties and to be sensitive to processing conditions. Mesoscale simulation techniques have helped to bridge the length and time scales needed to predict the microstructures of cured epoxies, but the prohibitive computational cost of simulating experimentally relevant system sizes has limited their impact. In this workwe develop an open-source plugin for the molecular dynamics code HOOMD-Blue that permits epoxy crosslinking simulations of millions of particles to be routinely performed on a single modern graphics card. Using these capabilities, we are able to use ensembles of epoxy processing pathways to obtain realistic bond kinetics and relaxation times that sensitively depend on stochastic bonding rates and a diffusive drag parameter respectively. This work also demonstrates the first implementation of fully customizable temperature-time curing profiles and the largest cross-linked structures obtained using molecular dynamics simulation. We evaluate coarse-grained models based on Dissipative Particle Dynamics (DPD) and compare with Lennard-Jones(LJ) models for their suitability to study glassy dynamics which is important for modeling epoxies or any other glassy material. We find that "hard" particle potentials such as the LJ potential are necessary to model glassy materials and characterize multiple viii methods for measuring the glass transition temperature (T g ) in simulations. We find that variations in temperature-time curing profiles result in significant differences in the final cured morphologies. Finally, we apply our general techniques to the specific DGEBA/DDS/PES system and validate our predicted glass transition temperatures against experiment.ix

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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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Computationally connecting organic photovoltaic performance to atomistic arrangements and bulk morphology

Cited by 26 publications

References 149 publications

Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly

New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics

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

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