In order to fit potential energy surface (PES) of gold nanoclusters, we have integrated bispectrum features with artificial neural network (ANN) learning technique in this work. We have also devised an algorithm for selecting the frequencies that need to be coupled for extracting the phase information between different frequency bands. We have found that higher order invariant like bispectrum is highly efficient in exploring the PES as compared to other invariants. The sensitivity of bispectrum can also be exploited in acting as an order parameter for calculating many thermodynamic properties of nanoclusters.

中文翻译：

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使用高阶不变量对纳米团簇的势能面进行建模的算法

为了适合金纳米团簇的势能面（PES），我们在这项工作中将双谱特征与人工神经网络（ANN）学习技术集成在一起。我们还设计了一种算法，用于选择需要耦合的频率以提取不同频段之间的相位信息。我们发现，与其他不变量相比，像双谱这样的高阶不变量在探索PES方面效率很高。双谱的敏感性还可以用作计算纳米团簇的许多热力学性质的有序参数。