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This research demonstrates that integrating uncertainty quantification into neural networks significantly improves the reliability of material property predictions, particularly when dealing with scarce data or out-of-distribution scenarios. By validating strategies like Gaussian negative log-likelihood loss and dropout-based Bayesian approximation across both classical models and graph neural networks, the study proves that enhancing model trustworthiness is a transferable capability applicable to complex tasks such as predicting coercivity from microstructural data. For practitioners in materials science and machine learning, this work underscores the critical importance of assessing predictive confidence to mitigate risks associated with data scarcity in high-stakes discovery processes.
Modelling magnetic material properties with uncertainty-aware neural networks
arXiv:2606.11870v1 Announce Type: cross Abstract: Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-quality data and the frequent need for out-of-distribution prediction introduce substantial uncertainty, making the assessment of model reliability essential. In this work, we investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research. In a first study, we benchmark classical and modern machine learning models for predicting intrinsic magnetic properties, focusing on the quality of their uncertainty estimates. We apply Gaussian negative log-likelihood loss and dropout-based Bayesian approximation as practical strategies for estimating predictive uncertainty. In a second study, we transfer these architectural features for uncertainty estimation to a more complex task: predicting coercivity from microstructural information using a graph neural network. Together, these studies demonstrate that uncertainty quantification not only enhances the trustworthiness of predictions but is also transferable across different modeling tasks.
Abstract:Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-quality data and the frequent need for out-of-distribution prediction introduce substantial uncertainty, making the assessment of model reliability essential. In this work, we investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research. In a first study, we benchmark classical and modern machine learning models for predicting intrinsic magnetic properties, focusing on the quality of their uncertainty estimates. We apply Gaussian negative log-likelihood loss and dropout-based Bayesian approximation as practical strategies for estimating predictive uncertainty. In a second study, we transfer these architectural features for uncertainty estimation to a more complex task: predicting coercivity from microstructural information using a graph neural network. Together, these studies demonstrate that uncertainty quantification not only enhances the trustworthiness of predictions but is also transferable across different modeling tasks.
Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2606.11870 [cond-mat.mtrl-sci]
(or arXiv:2606.11870v1 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2606.11870 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 176 words
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