The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly quantum mechanical methods. Through experiments with hafnium oxide data, we show that DPPs are competitive with existing approaches to constructing compact but diverse training sets by utilizing kernels of molecular descriptors, leading to improved accuracy and robustness in machine learning representations of molecular systems. Our work identifies promising directions to employ DPPs for unsupervised training data curation with heterogeneous or multimodal data, or in online active learning schemes for iterative data augmentation during molecular dynamics simulation.

翻译：机器学习原子间势的发展面临着关键的计算瓶颈，即有用训练数据集的生成与标注。我们提出将行列式点过程（DPP）创新应用于原子构型信息子集选择任务，通过昂贵的量子力学方法为这些子集标注参考能量与力。在氧化铪数据上的实验表明，通过利用分子描述符的核函数，DPP在与现有构建紧凑且多样化的训练集方法中具有竞争力，从而提升了机器学习分子系统表示的准确性与鲁棒性。本工作指明了利用DPP进行无监督训练数据策展（处理异质或多模态数据）以及分子动力学模拟中迭代数据增强的在线主动学习方案的有前景方向。