Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

Wodo, Olga; Żola, Jarosław; Pokuri, Balaji Sesha Sarath; Du, Pengfei; Ganapathysubramanian, Baskar

doi:10.1016/j.md.2015.12.001

Cited by 32 publications

(30 citation statements)

References 39 publications

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“…First, we can see how performance can be improved from a simple bilayer by increasing the amount of surface area between the acceptor and donor. 44 The maximum boost of performance is obtained when the donor(black) domains are fractal-like, 40 as shown in Fig. 5e.…”

Section: Morphology Designmentioning

confidence: 92%

“…Next, we add island type structures to inhibit performance. 44 In our example, a 'line' of donor is added to the existing morphology, creating several acceptor domains unconnected to the cathode. The performance suffers drastically as informed by the physics of photoconversion.…”

Section: Morphology Designmentioning

confidence: 99%

“…Previous analysis using this data can be found in. 44 A characteristic of this procedure for generating morphologies through simulation is their similarity to morphologies in real active layers produced during thermal annealing, for example, the domains are similar in size and have smooth interface contours. These characteristics will also help us to build trust in the training process by manually creating morphologies that break these characteristics and testing the performance of the trained network on such samples.…”

Section: Data Generation and Quantificationmentioning

confidence: 99%

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Interpretable deep learning for guided microstructure-property explorations in photovoltaics

et al. 2019

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The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.

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Section: Morphology Designmentioning

confidence: 92%

Section: Morphology Designmentioning

confidence: 99%

Section: Data Generation and Quantificationmentioning

confidence: 99%

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Interpretable deep learning for guided microstructure-property explorations in photovoltaics

et al. 2019

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“…Since the sizes and topology of the various phases in the PAL are theoretically expected to influence the optoelectronic properties, this should have an impact on the performance: the stability of the device and stability of the photoactive layer morphology are related. Even if to the best of our knowledge, a direct relationship between structure and optoelectronic performance has not been positively proven, structure-property correlations have been proposed [20,22,30] . P3HT-PCBM films containing micrometer-scaled crystals showing that the phase separation could promote crystal nucleation and growth, [87] Rathi investigated the competition between crystallization and LLPS in a crystalline-amorphous binary system, [88] and Saylor and Kim simulated the evaporation of an immiscible amorphous-crystalline system in solution with application to polymer-embedded drug-release films.…”

Section: Introductionmentioning

confidence: 99%

Role of the Interplay between Spinodal Decomposition and Crystal Growth in the Morphological Evolution of Crystalline Bulk Heterojunctions

Ronsin

Harting

2020

Energy Tech

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The stability of organic solar cells is strongly affected by the morphology of the photoactive layers, whose separated crystalline and/or amorphous phases are kinetically quenched far from their thermodynamic equilibrium during the production process. The evolution of these structures during the lifetime of the cell remains poorly understood. In this paper, a phase-field simulation framework is proposed, handling liquid-liquid demixing and polycrystalline growth at the same time in order to investigate the evolution of crystalline immiscible binary systems. We find that initially, the nuclei trigger the spinodal decomposition, while the growing crystals quench the phase coarsening in the amorphous mixture. Conversely, the separated liquid phases guide the crystal growth along the domains of high concentration. It is also demonstrated that with a higher crystallization rate, in the final morphology, single crystals are more structured and form percolating pathways for each material with smaller lateral dimensions. are known to show very poor efficiencies. More recently, for PCE11 (PffBT4T-2OD) -PCBM systems, demixing of donor and acceptor in the amorphous phase and subsequent aggregation of fullerenes has been proposed as the mechanism for strong burn-in degradation, on the basis of coupled GISAXS and GIWAXS experiments. [31] Different successful strategies have been then proposed to overcome this problem, such as adding a second acceptor, more compatible with the donor in order to stabilize the mixed phase [32][33][34][35] , the use of non-fullerene acceptors like IDTBR (rhodanine-benzothiadiazole-coupled indacenodithiophene) [36] or to kinetically quench the mixed phase. [37] These improvements are based on remarkable efforts to unravel the mechanisms of the PAL morphology formation and stability. On the one hand, they base on thermodynamic considerations such as miscibility of solvent, donor and acceptors [32,34,38,39] and the evaluation of phase diagrams [23][24]26,40,41] . On the other hand, the importance of the kinetic evolution towards the thermodynamic equilibrium during the deposition process and ageing, including possible transient or stable liquidliquid phase separation (LLPS) [42][43][44][45] , has been acknowledged and recently qualitatively taken into account for stability improvement [37] . Nevertheless, no general coherent physical framework has been proposed to understand the BHJ morphology formation and stability taking into account at the same time thermodynamic aspects such as liquid/amorphous phase stability and crystallinity and the kinetics of the system (kinetics + thermodynamics, crystallinity + miscibility). Therefore, the understanding of stability is still very material system-dependent. In the last decade, different simulation approaches have been proposed to contribute to the understanding of the BHJ formation and evolution in OPV systems. At the molecular scale, coarsegrained molecular dynamics (CGMD) [46][47][48] , dissipative particle dynamics (DPD) [49][50][51] or selfconsistent ...

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“…Particularly promising descriptors were: (i) fraction of domain that can absorb incident radiation weighted by the exciton generation profile (to characterize absorption), (ii) (Gaussian weighted) average distance from any donor region to the donor-acceptor interface (to characterize dissociation), and (iii) contact area of preferential material with respective electrode (to characterize charge transport). Earlier work has shown that the product of these absorption, dissociation, and charge transport descriptors can be used as a predictor for the short circuit current 114 . We chose around 100 morphologies with a relatively high short circuit current predictor to eliminate very poor morphologies (for instance with several islands).…”

Section: Extracting Morphology Traitsmentioning

confidence: 99%

Experimental and theoretical investigations of charge generation and transport in thin film photovoltaics

Gebhardt¹

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The performance of organic photovoltaic devices is improving steadily and efficiencies have now exceeded 10%. However, incident solar spectrum still largely remains poorly absorbed. To reduce optical losses, we employed a microlens array (MLA) layer on the side of the glass substrate facing the incident light; this approach does not interfere with the processing of the active-layer. We observed up to 10% enhancement in the short circuit current of poly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2b:4,5-b′]dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl] thieno[3,4b]thiophenediyl}):(6,6)-phenyl C71-butyric acid methyl ester (PTB7:PC71BM) OPV cells. Theoretically and experimentally investigating several MLA dimensions, we found that photocurrent increases with the ratio of height to pitch size of MLA. Simulations reveal the enhancement mechanisms: MLA focuses light, and also increases the light path within the active-layer by diffraction. Photocurrent enhancements increase for a polymer system with thinner active-layers, as † Contributed equally to this work. Y. Chen fabricated and characterized PTB7 based devices, and prepared the initial manuscript. M. Elshobaki fabricated and characterized PCDTBT based devices. R. Gebhardt fabricated and characterized the microlenses and also handled reviewer comments and finalization of the manuscript.

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Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

Cited by 32 publications

References 39 publications

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

Role of the Interplay between Spinodal Decomposition and Crystal Growth in the Morphological Evolution of Crystalline Bulk Heterojunctions

Experimental and theoretical investigations of charge generation and transport in thin film photovoltaics

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