An optimization approach to identify processing pathways for achieving tailored thin film morphologies

Pfeifer, Spencer; Wodo, Olga; Ganapathysubramanian, Baskar

doi:10.1016/j.commatsci.2017.11.040

Cited by 14 publications

(15 citation statements)

References 69 publications

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“…For example, while the predictive (computational) models used by materials scientists to establish quantitative PSPP relationships tend to be incomplete and are always computationally costly, when it comes to the evaluation of design choices, approximate solutions are often times sufficient. For example, work by Pfiefer et al [37] focuses on the process-structure linkage where they develop an integrated process-structure exploration framework designed to systematically identify viable processing conditions that result in tailored microstructures. The need to craft an appropriate cost function for optimization is noted as being critical to producing effective results and is emphasized in their work.…”

Section: Methodsmentioning

confidence: 99%

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Arróyave

Shields

Chang

et al. 2018

Journal of Mechanical Design

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We present the results from a workshop on interdisciplinary research on design of engineering material systems, sponsored by the National Science Foundation. The workshop was prompted by the need to foster a culture of interdisciplinary collaboration between the engineering design and materials communities. The workshop addressed the following: (i) conceptual barriers between materials and engineering design research communities; (ii) research questions that the interdisciplinary field of materials design should focus on; (iii) processes and metrics to be used to validate research activities and outcomes on materials design; and (iv) strategies to sustain and grow the interdisciplinary field. This contribution presents a summary of the state of the field-elicited through extensive guided discussions between representatives of both communities-and a snapshot of research activities that have emerged since the workshop. Based on the increasing level of sophistication of interdisciplinary research programs on design of materials it is apparent that the field is growing and has great potential to play a key role in a vibrant interdisciplinary materials innovation ecosystem. Sustaining such efforts will contribute significantly to the advancement of technologies that will impact many industries and will enhance society-wide health, security, and economic well-being.

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Section: Methodsmentioning

confidence: 99%

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Arróyave

Shields

Chang

et al. 2018

Journal of Mechanical Design

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“…The final properties of OEs (e.g., electrical conductivity) are a function of more than a dozen material and process variables that can be tuned (e.g., through evaporation rate, blend ratio of polymers, final film thickness, solubility, degree of polymerization, atmosphere, shearing stress, chemical strength, and frequency of patterning substrate), leading to the combinatorial explosion of manufacturing variants. Because the standard trial-and-error approach, in which many prototypes are manufactured and tested, is too slow and cost inefficient, scientists are investigating in silico approaches [39,40]. The idea is to describe the key physical processes via a set of differential equations, and then perform high-fidelity numerical simulations to capture the process dynamics in relation to input variables.…”

Section: Applicationmentioning

confidence: 99%

Learning Manifolds from Dynamic Process Data

et al. 2020

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Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understanding of the underlying processes requires mapping such data to a low-dimensional manifold where the dynamics of the latent processes are evident. While nonlinear spectral dimensionality reduction methods, e.g., Isomap, and their scalable variants, are conceptually fit candidates for obtaining such a mapping, the presence of the strong temporal correlation in the data can significantly impact these methods. In this paper, we first show why such methods fail when dealing with dynamic process data. A novel method, Entropy-Isomap, is proposed to handle this shortcoming. We demonstrate the effectiveness of the proposed method in the context of understanding the fabrication process of organic materials. The resulting low-dimensional representation correctly characterizes the process control variables and allows for informative visualization of the material morphology evolution.

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“…In our previous work [30], we introduced an integrated phase-field -particle swarm optimization routine to systematically identify fabrication conditions capable of producing a specified morphology, in a fullyautomated fashion. This process-to-structure model explored and optimized two fabrication parameters (annealing time and substrate patterning) to identify processing conditions capable of producing desirable microstructures.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In particular, the resultant microstructure is notably dependent on the solvent type [6,37] and evaporation profile [38][39][40][41]. The second processing parameter is the pattern of substrate surface chemistry [30]. Preferential wetting at the substrate can trigger surface-directed composition waves, influencing the final phase-separated morphology and often leading to surface enrichment layers [42,43].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Process optimization for microstructure-dependent properties in thin film organic electronics

Pfeifer

Pokuri

et al. 2018

Materials Discovery

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The processing conditions during solvent-based fabrication of thin film organic electronics significantly determine the ensuing microstructure. The microstructure, in turn, is one of the key determinants of device performance. In recent years, one of the foci in organic electronics has been to identify processing conditions for enhanced performance. This has traditionally involved either trial-and-error exploration, or a parametric sweep of a large space of processing conditions, both of which are time and resource intensive. This is especially the case when the process → structure and structure → property simulators are computationally expensive to evaluate. In this work, we integrate an adaptive-sampling based, gradient-free, Bayesian optimization routine with a phase-field morphology evolution framework that models solvent-based fabrication of thin film polymer blends (process → structure simulator) and a graph-based morphology characterization framework that evaluates the photovoltaic performance of a given morphology (structure → property simulator). The Bayesian optimization routine adaptively adjusts the processing parameters to rapidly identify optimal processing configurations, thus reducing the computational effort in process → structure → property explorations. This serves as a modular, parallel 'wrapper' framework that facilitates swapping-in other process simulators and device simulators for general process → structure → property optimization. We showcase this framework by identifying two processing parameters, the solvent evaporation rate and the substrate patterning wavelength, in a model system that results in a device with enhanced photovoltaic performance evaluated as the shortcircuit current of the device. The methodology presented here provides a modular, scalable and extensible approach towards the rational design of tailored microstructures with enhanced functionalities.

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An optimization approach to identify processing pathways for achieving tailored thin film morphologies

Cited by 14 publications

References 69 publications

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

Learning Manifolds from Dynamic Process Data

Process optimization for microstructure-dependent properties in thin film organic electronics

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