Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets,Journal of Computational Chemistry

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Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2022-11-16 , DOI: 10.1002/jcc.27035
Gunnar Schmitz ₁ , Bastian Schnieder ₁

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We present a Gaussian process regression (GPR) scheme with an adaptive regularization scheme applied to the QM7 and QM9 test set, several protonated water clusters and specifically to the problem of atomic hydrogen adsorption on graphene sheets. For the last system our goal is to achieve good predictive accuracy with only a few training points. Therefore, we assess for these systems a self-correcting multilayer GPR model, in which the prediction is corrected by a chain of additional GPR models. In our adaptive regularization scheme, we impose no noise on the training data, but use an approach based on the data itself to account for its impurity. The strength of this strategy is that the data points are treated differently based on their importance and that the regularization can still be controlled by a single parameter. We assess how the accuracy of the prediction depends on this parameter. We can show that the new regularization scheme as well as the multilayer approach results in more robust predictors. Furthermore, we demonstrate that the predictor can be in good agreement with the density-functional theory results.

中文翻译：

自适应正则化高斯过程回归在石墨烯片上氢吸附方面的应用

我们提出了一种高斯过程回归 (GPR) 方案，该方案具有适用于 QM7 和 QM9 测试集的自适应正则化方案，几个质子水簇，特别是石墨烯片上的原子氢吸附问题。对于最后一个系统，我们的目标是仅用几个训练点就可以达到良好的预测精度。因此，我们为这些系统评估了一个自校正多层 GPR 模型，其中预测由一系列额外的 GPR 模型进行校正。在我们的自适应正则化方案中，我们不对训练数据施加噪声，而是使用基于数据本身的方法来解释其不纯度。该策略的优势在于数据点根据其重要性进行不同的处理，并且正则化仍然可以由单个参数控制。我们评估预测的准确性如何取决于此参数。我们可以证明新的正则化方案以及多层方法会产生更强大的预测器。此外，我们证明了预测变量与密度泛函理论的结果非常吻合。

更新日期：2022-11-16

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