A probabilistic framework for crystal structure denoising, phase classification, and order parameters
📰 ArXiv cs.AI
Learn a probabilistic framework for denoising crystal structures, classifying phases, and extracting order parameters, and how to apply it to atomistic simulations
Action Steps
- Apply probabilistic modeling to denoise crystal structures using Bayesian inference
- Use the framework to classify phases in crystal structures based on probabilistic outputs
- Extract continuous order parameters from denoised crystal structures using the proposed methodology
- Evaluate the performance of the framework on benchmark datasets
- Integrate the framework into existing atomistic simulation pipelines to improve data analysis
Who Needs to Know This
Materials scientists and researchers working with atomistic simulations can benefit from this framework to improve the accuracy and robustness of their results
Key Insight
💡 A unified probabilistic framework can be used to denoise crystal structures, classify phases, and extract order parameters in a robust and general manner
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🔍 New probabilistic framework for crystal structure denoising, phase classification, and order parameters! 📈
Key Takeaways
Learn a probabilistic framework for denoising crystal structures, classifying phases, and extracting order parameters, and how to apply it to atomistic simulations
Full Article
Title: A probabilistic framework for crystal structure denoising, phase classification, and order parameters
Abstract:
arXiv:2512.11077v3 Announce Type: replace-cross Abstract: Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains challenging. Existing tools are often specialized to a limited set of prototypes and split thermal-noise removal, phase classification, and OP construction into separate steps. Here we present a unified probabilistic framework for analyzing noisy atomic configuratio
Abstract:
arXiv:2512.11077v3 Announce Type: replace-cross Abstract: Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains challenging. Existing tools are often specialized to a limited set of prototypes and split thermal-noise removal, phase classification, and OP construction into separate steps. Here we present a unified probabilistic framework for analyzing noisy atomic configuratio
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