Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting 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 band gap properties) show that the popular ensemble methods for uncertainty estimation is NOT the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and data sets can be accessed freely at \url{https://github.com/usccolumbia/materialsUQ}.
翻译:不确定性量化(UQ)在建立稳健的高性能和通用材料财产预测模型方面日益重要,也可以用于积极学习,通过侧重于从不确定区域获取新的培训数据来培训更好的模型。有些类别的UQ方法都考虑到不同类型的不确定性来源。我们在这里对基于图形神经网络的基于神经网络材料的图形神经材料财产预测方法进行了全面评估,并评价这些方法如何真正反映我们在错误估计或积极学习中想要的不确定性。我们对四个晶体材料数据集(包括形成能量、吸附能、总能量和波段间距特性)的实验结果显示,在材料财产预测中,常用的不确定性估算共同方法并不是UQ的最佳选择。为方便社区起见,所有源代码和数据集都可以在\url{https://github.com/uscolumbia/manualsU}上自由访问。