Modelling magnetic material properties with uncertainty-aware neural networks

arXiv:2606.11870 · cond-mat.mtrl-sci, cs.LG · Submitted 2026-06-10 · Read on arXiv

cond-mat.mtrl-sci, cs.LG

Submitted: 2026-06-10

Updated: 2026-10-05

Code: https://github.com/ax-ml/jax

Terminology

• Uncertainty Quantification — 2× in this paper · explained in 9 episodes
• Benchmark — 1× in this paper · explained in 2 episodes

Sources

• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Confidence curves for UQ validation: probabilistic reference vs. oracle
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction
• Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
• Aleatoric and Epistemic Uncertainty with Random Forests
• Fast Graph Representation Learning with PyTorch Geometric

Related papers

• Mode-selective electron-phonon coupling drives charge density waves in the kagome metals YRu 3 Si 2 and LaRu 3 Si 2
• Restoring the Surface Magnetic Gap in MnBi 2 Te 4
• ATLAS: A Foundation Neural Sampler for Amorphous Materials
• A Generalizable Machine Learning Framework for Half-Heusler Thermoelectrics
• Nonlinear dynamics in magnonic Fabry-P' e rot resonators: Low-power neuron-like activation and transmission suppression
• AES-Debye: an Accurate, Efficient, and Scalable Engine for Debye Scattering Calculations