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End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural

Ryan Cohn¹, Elizabeth A Holm²

¹Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA.

Scientific Reports

|December 5, 2024

Summary

Graph neural networks accurately predict abnormal grain growth, outperforming computer vision methods. This research offers insights into microstructure features influencing grain growth.

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Area of Science:

Materials Science
Computational Materials Science
Data Science

Background:

Abnormal grain growth is a challenging phenomenon in materials science due to its stochastic nature.
Predicting abnormal grain growth is crucial for controlling material properties.
Traditional methods struggle with the inherent complexity and randomness of this process.

Purpose of the Study:

To evaluate the effectiveness of graph neural networks (GNNs) in predicting abnormal grain growth.
To compare GNN performance against standard computer vision techniques for microstructure analysis.
To gain physical insights into the factors governing abnormal grain growth.

Main Methods:

Generation of a large dataset using Monte Carlo simulations of abnormal grain growth.
Training simple graph convolution networks on initial microstructures.
Comparison with a computer vision approach for prediction accuracy and false positives.

Main Results:

Graph neural networks achieved 73% prediction accuracy for abnormal grain growth.
GNNs outperformed the computer vision method, showing fewer false positives.
The study identified important microstructural features and relevant length scales for prediction.

Conclusions:

Graph neural networks offer a promising and accurate approach for predicting abnormal grain growth.
The GNN model provides valuable physical insights into the underlying mechanisms.
Further analysis of simulation uncertainty can enhance future predictive models.