In this work, we develop two main Machine Learning based approaches to predict the runtime parameters of highly scalable parallel chemistry computations.These approaches employ active and generative learning together with the empirically determined gradient boosted regression tree models chosen among a rich suite of machine learning models. When evaluated on Coupled-Cluster with Singles and Doubles computations, our models achieve a mean absolute error percentage (MAPE) as low as 0.023 and a coefficient of determination as high as 99.9%. Furthermore, when combined with active learning to mitigate the lack of large amounts of training data, our models score a MAPE about 0.2 with 20-25% of the original dataset.

翻译：在本工作中，我们开发了两种基于机器学习的方法，用于预测高度可扩展的并行化学计算的运行时参数。这些方法将主动学习和生成式学习与经验确定的梯度提升回归树模型相结合，该模型从丰富的机器学习模型套件中选出。在单双激发耦合簇计算上的评估显示，我们的模型实现了低至0.023的平均绝对误差百分比（MAPE）和高达99.9%的决定系数。此外，当结合主动学习以缓解大量训练数据不足的问题时，我们的模型在使用原始数据集的20-25%时达到了约0.2的MAPE。