弱束缚复合物势能面的回归树系综主动学习
Active learning of potential-energy surfaces of weakly-bound complexes with regression-tree ensembles
摘要：
几个池为基础的主动学习算法（AL）被用来模拟势能面（PES）的电子结构计算的最小数量。理论和经验的结果表明，优越的策略，可以通过采样分子结构对应的大的不确定性，在他们的预测，而在同一时间不偏离数据的真实分布。为了在AL框架中对PES进行建模，我们建议使用森林随机查询的回归版本，这是一种混合方法，可以对对应于大不确定性的点进行采样，同时避免从稀疏空间区域收集太多点。该算法是用决策树来实现的，计算成本相对较小。我们的经验表明，该算法需要大约一半的数据收敛到相同的精度相比，基于不确定性的查询由委员会算法。此外，该算法是全自动的，不需要任何先验知识的PES。在\pyrrolew的6D PES上的仿真结果表明，$\mathord{<}15\,000$配置足以建立一个泛化误差为16 \invcm的PES，而最终模型约有50 000个配置，泛化误差为11 \invcm .
英文摘要：
Several pool-based active learning algorithms (AL) were employed to model potential energy surfaces (PESs) with a minimum number of electronic structure calculations. Theoretical and empirical results suggest that superior strategies can be obtained by sampling molecular structures corresponding to large uncertainties in their predictions while at the same time not deviating much from the true distribution of the data. To model PESs in an AL framework we propose to use a regression version of stochastic query by forest, a hybrid method that samples points corresponding to large uncertainties while avoiding collecting too many points from sparse regions of space. The algorithm is implemented with decision trees that come with relatively small computational costs. We empirically show that this algorithm requires around half the data to converge to the same accuracy in comparison to the uncertainty-based query-by-committee algorithm. Moreover, the algorithm is fully automatic and does not require any prior knowledge of the PES. Simulations on a 6D PES of \pyrrolew show that $\mathord{<}15\,000$ configurations are enough to build a PES with a generalization error of 16~\invcm, whereas the final model with around 50\,000 configurations has a generalization error of 11~\invcm.
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