Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose a gradient boosting decision tree (GBDT)-based framework for the automatic selection of optimal ACSFs. This framework takes uniformly distributed sets of ACSFs as input and evaluates their relative importance. The ACSFs with high average importance scores are selected and used to train an ANN potential. We applied this method to the Ge system, resulting in an ANN potential with root-mean-square errors (RMSE) of 10.2 meV/atom for energy and 84.8 meV/Å for force predictions, utilizing only 18 ACSFs to achieve a balance between accuracy and computational efficiency. The framework is validated using the grid searching method, demonstrating that ACSFs selected with our framework are in the optimal region. Furthermore, we also compared our method with commonly used feature selection algorithms. The results show that our algorithm outperforms the others in terms of effectiveness and accuracy. This study highlights the significance of the ACSF parameter effect on the ANN performance and presents a promising method for automatic ACSF selection, facilitating the development of machine learning potentials.

中文翻译：

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使用 Gradient Boosting Decision 算法自动选择以原子为中心的神经网络势。


原子中心神经网络 （ANN） 势在原子系统建模中表现出高精度和计算效率。开发可靠的 ANN 电位的关键步骤是正确选择以原子为中心的对称函数 （ACSF），也称为原子特征，来描述原子环境。ACSF 选择不当会导致 ANN 电位质量差。在这里，我们提出了一个基于梯度提升决策树 （GBDT） 的框架，用于自动选择最佳 ACSF。此框架将均匀分布的 ACSF 集作为输入，并评估它们的相对重要性。选择平均重要性得分较高的 ACSF 并用于训练 ANN 潜力。我们将这种方法应用于 Ge 系统，产生了能量均方根误差 （RMSE） 为 10.2 meV/atom 和力预测为 84.8 meV/Å 的 ANN 电位，仅使用 18 个 ACSF 实现了精度和计算效率之间的平衡。该框架使用网格搜索方法进行验证，表明使用我们的框架选择的 ACSF 处于最佳区域。此外，我们还将我们的方法与常用的特征选择算法进行了比较。结果表明，我们的算法在有效性和准确性方面优于其他算法。本研究强调了 ACSF 参数效应对 ANN 性能的重要性，并提出了一种有前途的自动 ACSF 选择方法，促进了机器学习潜力的发展。