How to use self supervised machine learning models to use experimental data to detect phase transitions?
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Self-supervised machine learning models can be used to detect phase transitions in experimental data. These models can accurately classify different types of phase transitions by analyzing the fluctuation properties of machine learning outputs . By employing techniques such as self-supervised ensemble learning (SSEL), these models can discern first-order, second-order, and Berezinskii-Kosterlitz-Thouless transitions . The SSEL approach can also be applied to investigate quantum phase transitions . These models simulate special state functions with higher-order correlations between physical quantities, providing richer information than previous machine learning methods . The frameworks based on self-supervised learning, such as relational reasoning and contrastive learning, can accurately identify phase transitions using a limited amount of labeled data . The selection of appropriate data augmentation techniques is crucial to retain scientifically meaningful information . These self-supervised machine learning models can be used to classify phases and obtain phase diagrams .
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| Papers (4) | Insight |
|---|---|
28 May 2023 | The provided paper does not discuss the use of self-supervised machine learning models to detect phase transitions using experimental data. |
28 Feb 2023 | The paper discusses the use of self-supervised learning models to classify spectral data and detect phase transitions in x-ray diffraction experiments. It mentions that appropriate data augmentations and pretext tasks are crucial for the success of these models. However, it does not provide specific details on how to use these models with experimental data to detect phase transitions. |
30 Jun 2023 | The provided paper does not specifically mention using experimental data to detect phase transitions using self-supervised machine learning models. The paper focuses on using in-situ spin configurations as input features to classify phase transitions in different models. |
| The paper discusses the use of self-supervised learning models to classify X-ray diffraction spectra during phase transitions. It introduces three frameworks based on self-supervised learning that can accurately identify phase transitions using data transformations and a small amount of labeled data. The paper also emphasizes the importance of selecting appropriate data augmentation techniques to retain scientifically meaningful information. However, the paper does not provide specific details on how to use self-supervised machine learning models to detect phase transitions using experimental data. |
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