Leveraging machine learning to harness non-parabolic effects in semiconductor heterostructures

Macedo, Gabriel da Silva; Dias, Mariama Rebello Sousa; Bezerra, Anibal Thiago

doi:10.1016/j.physe.2022.115513

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The ground state energy and effective mass are usually properties closely related to a quantum well's electronic structure. Da Silva Macedo et al [109] used ML to rapidly and accurately predict the electronic properties of quantum well semiconductor heterostructures. The authors obtained the eigenstate energies and effective masses for thousands of heterostructure configurations to train an ANN model.…”

Section: Electronic Structurementioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Section: Electronic Structurementioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…Yüksel et al applied multilayer perceptron architectures to predict the ground-state binding energies of atomic nuclei [33]. In a study by da Silva Macedo et al an NN was trained to predict the energy levels and energy-dependent masses as nonparabolic properties of semiconductor heterostructures [34]. The learning ability of a physics-informed proper orthogonal decomposition-Galerkin simulation methodology for QD structures was investigated by Veresko and Cheng [35].…”

Section: Introductionmentioning

confidence: 99%

Deep learning neural network for approaching Schrödinger problems with arbitrary two-dimensional confinement

Radu,

Duque

2023

Mach. Learn.: Sci. Technol.

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This article presents an approach to the two-dimensional Schrödinger equation based on automatic learning methods with neural networks. It is intended to determine the ground state of a particle confined in any two-dimensional potential, starting from the knowledge of the solutions to a large number of arbitrary sample problems. A network architecture with two hidden layers is proposed to predict the wave function and energy of the ground state. Several accuracy indicators are proposed for validating the estimates provided by the neural network. The testing of the trained network is done by applying it to a large set of confinement potentials different from those used in the learning process. Some particular cases with symmetrical potentials are solved as concrete examples, and a good network prediction accuracy is found.

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Leveraging machine learning to harness non-parabolic effects in semiconductor heterostructures

Cited by 2 publications

References 22 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Deep learning neural network for approaching Schrödinger problems with arbitrary two-dimensional confinement

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