Uncertainty quantification (UQ) has increasing importance in the building of robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on gathering new training data from uncertain regions. There are several categories of UQ methods, each considering different types of uncertainty sources. Here, we conduct a comprehensive evaluation on the UQ methods for graph neural network-based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and bandgap properties) show that the popular ensemble methods for uncertainty estimation are NOT always the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and datasets can be accessed freely at https://github.com/usccolumbia/materialsUQ.

中文翻译：

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具有不确定性量化的材料性能预测：基准研究

不确定性量化 (UQ) 在构建稳健的高性能和可推广的材料特性预测模型中越来越重要。它还可以用于主动学习，通过专注于从不确定区域收集新的训练数据来训练更好的模型。UQ 方法有多种类别，每种方法都考虑不同类型的不确定性来源。在这里，我们对用于基于图神经网络的材料特性预测的 UQ 方法进行了全面评估，并评估它们如何真正反映我们在误差边界估计或主动学习中想要的不确定性。我们在四个晶体材料数据集（包括形成能、吸附能、总能、和带隙特性）表明，用于不确定性估计的流行集成方法并不总是 UQ 在材料特性预测中的最佳选择。为了社区的方便，所有源代码和数据集都可以在 https://github.com/usccolumbia/materialsUQ 免费访问。