论文标题
自动增长的全球反应性神经网络势能表面：一种无轨迹的主动学习策略
Automatically growing global reactive neural network potential energy surfaces: a trajectory free active learning strategy
论文作者
论文摘要
提出了一种有效且无轨迹的主动学习方法，以自动采样数据点，用于使用神经网络（NNS）构建全球精确的反应性势能表面（PESS）。尽管NNS没有像高斯过程回归那样提供预测方差，但我们可以最大程度地减少两个不同NN模型给出的平方差表面（NSD）的负，以积极定位PES最自信的点。该NSD的最小值中的一批点可以迭代地添加到训练集中，以改善PES。配置空间逐渐和全球覆盖，无需运行经典轨迹（或等效地分子动力学）模拟。通过重新安装H3和OH3反应性系统的可用分析PES，我们证明了这种新策略的效率和鲁棒性，从而在量子散射概率方面可以快速收敛相对于点数的快速收敛。
An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The configuration space is gradually and globally covered with no need to run classical trajectory (or equivalently molecular dynamics) simulations. Through refitting the available analytical PESs of H3 and OH3 reactive systems, we demonstrate the efficiency and robustness of this new strategy, which enables fast convergence of the reactive PESs with respect to the number of points in terms of quantum scattering probabilities.
