用不确定性感知神经网络提升磁性材料预测可靠性
Modelling magnetic material properties with uncertainty-aware neural networks
- 采用高斯负对数似然和丢弃率贝叶斯近似量化预测不确定性
- 在磁性材料属性预测中，不确定性估计显著提升模型可信度
- 方法可迁移至微观结构到矫顽力的复杂任务，适用材料研发领域
机器学习正被广泛用于加速新材料发现，通过探索大规模成分与结构设计空间。然而，高质量数据稀缺及频繁出现分布外预测带来显著不确定性，因此评估模型可靠性至关重要。本文研究不确定性量化在永磁材料研究中的应用。首先，对比经典与现代机器学习模型在预测本征磁性属性时的不确定性估计质量，采用高斯负对数似然损失与基于丢弃率的贝叶斯近似作为实际策略。其次，将这些不确定性估计架构迁移至更复杂的任务：利用图神经网络从微观结构信息预测矫顽力。两项研究共同表明，不确定性量化不仅能增强预测可信度，且具备跨任务可迁移性。
原文摘要 · Abstract (English)
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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