Modelling magnetic material properties with uncertainty-aware neural networks
Machine learning is increasingly used to accelerate materials discovery across large compositional and structural design spaces, but limited and heterogeneous data make reliable uncertainty estimation essential. In this work, we investigate uncertainty quantification for permanent magnet modeling across three complementary studies. First, on a public Curie temperature surrogate dataset, we benchmark Gaussian process regression, random forest bagging, and dropout-based Bayesian neural networks and show that calibration, sharpness, and confidence curve diagnostics reveal differences in uncertainty quality that are not visible from point-prediction metrics alone. Second, we transfer this fra...
| Authors | Clemens Wager, Heisam Moustafa, Alexander Kovacs et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 10 June 2026 |
| Last updated at source | 5 October 2026 |
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