Modelling magnetic material properties with uncertainty-aware neural networks

• URL: http://arxiv.org/abs/2606.11870v1
• Date: Wed, 10 Jun 2026 09:47:21 GMT
• Title: Modelling magnetic material properties with uncertainty-aware neural networks
• Abstract summary: We investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research.<n>In a first study, we benchmark classical and modern machine learning models for predicting intrinsic magnetic properties.<n>In a second study, we transfer these architectural features for uncertainty estimation to a more complex task: predicting coercivity from microstructural information.
• Score: 28.439523950142092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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