Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Dropka, Natasha; Holeňa, Martin

doi:10.3390/cryst10080663

Cited by 21 publications

(16 citation statements)

References 48 publications

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“…ANNs architectures (or topologies) are versatile and can be built for clustering, classification, regression, or dimensionality reduction . ANNs are prone to overfit data due to their high number of parameters.…”

Section: Overview Of Machine Learning Algorithms and Techniquesmentioning

confidence: 99%

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

et al. 2022

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Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

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Section: Overview Of Machine Learning Algorithms and Techniquesmentioning

confidence: 99%

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

et al. 2022

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“…The Euclidean distance of the intensity difference between each peak of the obtained XRD was used for reference, and the XRD for the experiment was calculated. The Euclidean distance dĲref,exp) between the literature values and experimental values is expressed as formula (1).…”

Section: Data Set Preparationmentioning

confidence: 99%

“…There have been several reports on the use of machine learning in crystal growth research. 1 Tsunooka et al succeeded in rapidly predicting the convection in a melt using machine learning. 2 Boucetta et al showed that the measurement of the location around the reaction vessel in the furnace, that is, the region with a high-temperature thermal gradient, is important for predicting the thermal distribution in the furnace.…”

Section: Introductionmentioning

confidence: 99%

Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning

et al. 2022

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“…Besides validating heating profiles, many detailed validations for the ingot properties are essential for this integrated framework. 26 …”

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

et al. 2022

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The casting mono-like silicon (Si) grown by directional solidification (DS) is promising for high-efficiency solar cells. However, high dislocation clusters around the top region are still the practical drawbacks, which limit its competitiveness to the monocrystalline Si. To optimize the DS-Si process, we applied the framework, which integrates the growing experiments, transient global simulations, artificial neuron network (ANN) training, and genetic algorithms (GAs). First, we grew the Si ingot by the original recipe and reproduced it with transient global modeling. Second, predictions of the Si ingot domain from different recipes were used to train the ANN, which acts as the instant predictor of ingot properties from specific recipes. Finally, the GA equipped with the predictor searched for the optimal recipe according to multi-objective combination, such as the lowest residual stress and dislocation density. We also implemented the optimal recipe in our mono-like DS-Si process for verification and comparison. According to the optimal recipe, we could reduce the dislocation density and smooth the growth rate during the Si ingot growing process. Comparisons of the growth interface and grain boundary evolutions showed the decrease of the interface concavity and the multi-crystallization in the top part of the ingot. The well-trained ANN combined with the GA could derive the optimal growth parameter combinations instantly and quantitatively for the multi-objective processes.

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Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Cited by 21 publications

References 48 publications

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning

Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

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Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Cited by 21 publications

References 48 publications

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Importance of raw material features for the prediction of flux growth of Al2O3 crystals using machine learning

Data-Driven Optimization and Experimental Validation for the Lab-Scale Mono-Like Silicon Ingot Growth by Directional Solidification

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Importance of raw material features for the prediction of flux growth of Al₂O₃ crystals using machine learning