LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns

LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns

Abstract In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diff...

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Bibliographic Details
Main Authors: Mehran Motamedi, Reza Shidpour, Mehdi Ezoji
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Reports
Subjects:
Long short-term memory
Sliding window technique
Sequence length
Number of units
Nuance effect
Online Access:https://doi.org/10.1038/s41598-024-75783-6
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https://doi.org/10.1038/s41598-024-75783-6

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