NURBS-based microstructure design for organic photovoltaics

Noruzi, Ramin; Ghadai, Sambit; Bingol, Onur Rauf; Krishnamurthy, Adarsh; Ganapathysubramanian, Baskar

doi:10.1016/j.cad.2019.102771

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Over the past decade, a wide variety of approaches have been explored for solving the inverse microstructure design problem [3][4][5] . Traditionally, iterative optimization approaches have been the most popular strategy to search for microstructures that yield desirable characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…The push to democratize microstructure-sensitive design led to efforts focused on relaxing the challenges described above. Some approaches relied on developing cheaper, but less accurate surrogates for the forward model (example, using graphs 3 ), while other approaches made the problem computationally tractable by severely constraining the design space (allowing only specific parameterized shapes) 4 .…”

Section: Introductionmentioning

confidence: 99%

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Fast inverse design of microstructures via generative invariance networks

et al. 2021

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The problem of efficient design of material microstructures exhibiting desired properties spans a variety of engineering and science applications. An ability to rapidly generate microstructures that exhibit user-specified property distributions transforms the iterative process of traditional microstructure-sensitive design. We reformulate the microstructure design process as a constrained Generative Adversarial Network (GAN). This approach explicitly encodes invariance constraints within a GAN to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, various short circuit current density and fill-factor combinations. Such invariance constraints can be represented by deep learning-based surrogates of full physics models mapping microstructure to photovoltaic properties. To circumvent data generation bottlenecks, we utilize a multi-fidelity surrogate that reduces the requirements of expensive labels by 5X. Our approach enables fast generation of microstructures (in ≈190ms) with user-defined properties. Such physics-aware data-driven methods for inverse design problems are expected to democratize and accelerate the field of microstructure-sensitive design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast inverse design of microstructures via generative invariance networks

et al. 2021

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Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

Jiang,

Yao,

Yang

et al. 2024

Solar RRL

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Organic solar cells (OSCs), renowned for their lightweight, cost efficiency, and adaptability nature, stand out as a promising option for developing renewable energy. Improving the power conversion efficiency (PCE) of OSCs is essential, and researchers are delving into novel materials to achieve this. Traditional approaches are often laborious and costly, highlighting the need for predictive modeling. Machine learning (ML), especially via quantitative structure–property relationship (QSPR) models, is streamlining material development, with a goal to exceed a 20% PCE. In this review, the application of ML in OSCs is explored, and recent studies utilizing ML approaches for PCE prediction are reviewed, encompassing empirical functions, ML algorithms, self‐devised ML frameworks, and the combination with automated experimental technologies. First, the benefits of ML in predicting PCE for OSCs are addressed. Second, the development of high‐efficiency predictive models for both fullerene and nonfullerene acceptors is delved into. The impact of various ML algorithm models on PCE prediction is then assessed, taking into account the construction of predictive models. Moreover, the quality of databases and the selection of descriptors are considered. Databases and descriptors based on experimental studies are further categorized. Finally, prospects for the future development of OSCs are proposed.

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