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Full-Text Articles in Engineering
Perspective On Coarse-Graining, Cognitive Load, And Materials Simulation, Eric Jankowski, Nealee Ellyson, Jenny W. Fothergill, Michael M. Henry, Mitchell H. Leibowitz, Evan D. Miller, Mone't Alberts, Jamie D. Guevara, Chris D. Jones, Mia Klopfenstein, Kendra K. Noneman, Rachel Singleton, Matthew L. Jones
Perspective On Coarse-Graining, Cognitive Load, And Materials Simulation, Eric Jankowski, Nealee Ellyson, Jenny W. Fothergill, Michael M. Henry, Mitchell H. Leibowitz, Evan D. Miller, Mone't Alberts, Jamie D. Guevara, Chris D. Jones, Mia Klopfenstein, Kendra K. Noneman, Rachel Singleton, Matthew L. Jones
Materials Science and Engineering Faculty Publications and Presentations
The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be …
Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski
Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski
Materials Science and Engineering Faculty Publications and Presentations
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, …
Tying Together Multiscale Calculations For Charge Transport In P3ht: Structural Descriptors, Morphology, And Tie-Chains, Evan D. Miller, Matthew L. Jones, Eric Jankowski
Tying Together Multiscale Calculations For Charge Transport In P3ht: Structural Descriptors, Morphology, And Tie-Chains, Evan D. Miller, Matthew L. Jones, Eric Jankowski
Materials Science and Engineering Faculty Publications and Presentations
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 …
Optimization And Validation Of Efficient Models For Predicting Polythiophene Self-Assembly, Evan D. Miller, Matthew L. Jones, Michael M. Henry, Paul Chery, Kyle Miller, Eric Jankowski
Optimization And Validation Of Efficient Models For Predicting Polythiophene Self-Assembly, Evan D. Miller, Matthew L. Jones, Michael M. Henry, Paul Chery, Kyle Miller, Eric Jankowski
Materials Science and Engineering Faculty Publications and Presentations
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 …
Computationally Connecting Organic Photovoltaic Performance To Atomistic Arrangements And Bulk Morphology, Matthew L. Jones, Eric Jankowski
Computationally Connecting Organic Photovoltaic Performance To Atomistic Arrangements And Bulk Morphology, Matthew L. Jones, Eric Jankowski
Materials Science and Engineering Faculty Publications and Presentations
Rationally designing roll-to-roll printed organic photovoltaics requires a fundamental understanding of active layer morphologies optimized for charge separation and transport, and which ingredients can be used to self-assemble those morphologies. In this review article we discuss advances in three areas of computational modeling that provide insight into active layer morphology and the charge transport properties that result. We explain the computational bottlenecks prohibiting atomistically-detailed simulations of device-scale active layers and the coarse-graining and hardware acceleration strategies for overcoming them. We review coarse-grained simulations of organic photovoltaic active layers and show that high throughput simulations of experimentally-relevant length scales are now …