论文标题
部分可观测时空混沌系统的无模型预测
Active learning strategies for atomic cluster expansion models
论文作者
论文摘要
原子聚类扩展（ACE）最近提议是具有正式完整基集的新型数据驱动的原子间电位。由于任何原子间潜力的发展都需要仔细选择培训数据和彻底验证，因此非常需要培训数据集的构建以及模型不确定性的指示。在这项工作中，我们比较了两种方法的性能，以表明基于D-急剧标准和集合学习的ACE模型的不确定性指示。尽管两种方法均显示出可比的预测，但基于D-彻底的（MAXVOL算法）的外推等级在计算上更有效。此外，外推级指标可以主动探索新结构，从而为自动发现稀有事件配置开辟了道路。我们证明，主动学习也适用于大规模MD模拟的局部原子环境。
The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven interatomic potentials with a formally complete basis set. Since the development of any interatomic potential requires a careful selection of training data and thorough validation, an automation of the construction of the training dataset as well as an indication of a model's uncertainty are highly desirable. In this work, we compare the performance of two approaches for uncertainty indication of ACE models based on the D-optimality criterion and ensemble learning. While both approaches show comparable predictions, the extrapolation grade based on the D-optimality (MaxVol algorithm) is more computationally efficient. In addition, the extrapolation grade indicator enables an active exploration of new structures, opening the way to the automated discovery of rare-event configurations. We demonstrate that active learning is also applicable to explore local atomic environments from large-scale MD simulations.
