Key Laboratory of Materials Modification by Laser, Electron, and Ion Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, P. R. China.
Phys Chem Chem Phys. 2022 Jun 1;24(21):12827-12836. doi: 10.1039/d2cp00719c.
There has been increasing attention in using machine learning technologies, such as neural networks (NNs) and Gaussian process regression (GPR), to model multi-dimensional potential energy surfaces (PESs). A PES constructed using NNs features high accuracy and generalization capability, but a single NN cannot actively select training points as GPR does, resulting in expensive calculations as the molecular complexity increases. However, a PES constructed using GPR has a slow speed of evaluation and it is difficult to accurately describe a fast-changing potential. Herein, an efficient scheme for representing globally accurate reactive PESs with complex topography based on as few points as possible by incorporating active data selection of GPR into NN fitting is proposed. The validity of this strategy is tested using the BeH system, and only 1270 points are automatically sampled. The generalization performance and speed of evaluation of the generated PES are much better than those of the GPR PES constructed using the same dataset. Moreover, an accurate NN PES is fitted by 12 122 points as a benchmark for comparison to further test the global accuracy of the PES obtained using this scheme, and the corresponding results present extremely consistent topography characteristics and calculated Be(S) + H reaction probabilities.
人们越来越关注使用机器学习技术，如神经网络（NNs）和高斯过程回归（GPR），来对多维势能面（PESs）进行建模。使用 NNs 构建的 PES 具有高精度和泛化能力，但单个 NN 不能像 GPR 那样主动选择训练点，因此随着分子复杂度的增加，计算成本会很高。然而，使用 GPR 构建的 PES 具有评估速度慢的特点，并且很难准确描述快速变化的势能。在此，提出了一种通过将 GPR 的主动数据选择纳入 NN 拟合来表示具有复杂地形的全局精确反应 PES 的有效方案，该方案只需尽可能少的点即可实现。通过使用 BeH 系统测试了该策略的有效性，仅自动采样了 1270 个点。生成的 PES 的泛化性能和评估速度明显优于使用相同数据集构建的 GPR PES。此外，拟合了一个准确的 NN PES，使用 12122 个点作为基准进行比较，以进一步测试使用该方案获得的 PES 的全局准确性，相应的结果呈现出极其一致的地形特征和计算得到的 Be(S) + H 反应概率。
