Computational intelligence applied to the growth of quantum dots

Singulani, Anderson; Neto, Omar P. Vilela; Pacheco, Marco C. Aurélio; Vellasco, Marley; Pires, M. P.; Souza, P. L.

doi:10.1016/j.jcrysgro.2008.07.065

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…An early study by Singulani et al, published in 2008, demonstrated the utility of ML in QD development. 111 Here, the authors applied an artificial neural network (ANN) and a…”

Section: Advanced Data Analysis and Predictionmentioning

confidence: 99%

“…An early study by Singulani et al ., published in 2008, demonstrated the utility of ML in QD development. 111 Here, the authors applied an artificial neural network (ANN) and a genetic algorithm (GA) to associate the height of epitaxially-grown InAs QDs with the synthesis parameters. In the epitaxial approach, QDs are formed by epitaxial deposition of semiconductor heterostructures (by e.g.…”

Section: How Can Data-led Strategies Help Us Overcome the Challenges ...mentioning

confidence: 99%

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Accelerating colloidal quantum dot innovation with algorithms and automation

et al. 2022

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“…An early study by Singulani et al, published in 2008, demonstrated the utility of ML in QD development. 111 Here, the authors applied an artificial neural network (ANN) and a…”

Section: Advanced Data Analysis and Predictionmentioning

confidence: 99%

Section: How Can Data-led Strategies Help Us Overcome the Challenges ...mentioning

confidence: 99%

Accelerating colloidal quantum dot innovation with algorithms and automation

et al. 2022

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“…applied ANN and genetic algorithm (GA) to optimize the growth conditions of epitaxial QDs. [ 42 ] During the modeling, the ANN has six different inputs: the indium flux in the reactor, the growth temperature, the deposition time, the width of the layer on top of which the dots are nucleated, the aluminum and indium content in the InGaAlAs with the mean height of resulting QD selected as the output. Figure 4 shows the comparison of the NN prediction for the QDs mean height and the obtained experimental data.…”

Section: Applications Of Machine Learning In Quantum Dot's Researchmentioning

confidence: 99%

How Machine Learning Accelerates the Development of Quantum Dots?^†

et al. 2020

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Quantum dots | Machine learning | Materials genome initiative | Neural networks | On-demand With the rapid developments in the field of information technology, the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and process development. Machine learning emerges as a new research paradigm to accelerate the application-oriented material discovery. Quantum dots are expanded as functional nanomaterials to enhance cutting-edge photonic technology. However, they suffer from uncertainty in industrial fabrication and application. Here, we discuss how machine learning accelerates the development of quantum dots. The basic principles and operation procedures of machine learning are described with a few representative examples of quantum dots. We emphasize how machine learning contributes to the optimization of synthesis and the analysis of material characterizations. To conclude, we give a short perspective discussing the problems of combining machine learning and quantum dots.

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“…Even though, for composites and nanocomposites, it's not possible to extract deep physical insights on the interfacial interaction between matrix and filler from the ANN. However, one can obtain trends that can help in constructing new physical models or in understanding the composite manufacturing process, as already made for the growth of other nanostructures, such as quantum dots [13].…”

Section: Nanocompositesmentioning

confidence: 99%

Modeling the young modulus of nanocomposites: A neural network approach

Cupertino

Neto

Pacheco

et al. 2011

The 2011 International Joint Conference on Neural Networks

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Composite materials have changed the way of using polymers, as the strength was favored by the incorporation of fibers and particles. This new class of materials allowed a larger number of applications. The insertion of nanometric sized particles has enhanced the variation of properties with a smaller load of fillers. In this paper, we attempt to a better understanding of nanocomposites by using an artificial intelligence's technique, known as artificial neural networks. This technique allowed the modeling of Young's modulus of nanocomposites. A good approximation was obtained, as the correlation between the data and the response of the network was high, and the error percentage was low.

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Computational intelligence applied to the growth of quantum dots

Cited by 5 publications

References 5 publications

Accelerating colloidal quantum dot innovation with algorithms and automation

Accelerating colloidal quantum dot innovation with algorithms and automation

How Machine Learning Accelerates the Development of Quantum Dots?^†

Modeling the young modulus of nanocomposites: A neural network approach

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Computational intelligence applied to the growth of quantum dots

Cited by 5 publications

References 5 publications

Accelerating colloidal quantum dot innovation with algorithms and automation

Accelerating colloidal quantum dot innovation with algorithms and automation

How Machine Learning Accelerates the Development of Quantum Dots?†

Modeling the young modulus of nanocomposites: A neural network approach

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Product

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How Machine Learning Accelerates the Development of Quantum Dots?^†