Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces.,The Journal of Physical Chemistry A

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Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces.
The Journal of Physical Chemistry A ( IF 2.7 ) Pub Date : 2020-08-20 , DOI: 10.1021/acs.jpca.0c05935
Mohamed Ali Boussaidi _{1,

2} , Owen Ren _{1,

3} , Dmitry Voytsekhovsky ₃ , Sergei Manzhos ₁

Affiliation

We present an approach combining a representation of a multivariate function using subdimensional functions with machine learning based representation of component functions: Random sampling high dimensional model representation Gaussian process regression (RS-HDMR-GPR). The use of Gaussian process regressions to represent component functions allows nonparametric (unbiased) representation and the possibility to work only with functions of desired dimensionality, obviating the need to build an expansion over orders of coupling. All component functions are determined from a single set of samples. The method is tested by fitting six- and 15-dimensional potential energy surfaces (PES) of polyatomic molecules as well as by computing vibrational spectra for a four-atomic molecule.

中文翻译：

多元函数表示的随机抽样高维模型表示高斯过程回归（RS-HDMR-GPR）：在分子势能表面上的应用。

我们提出了一种方法，该方法将使用多维函数的表示与基于机器学习的组件函数表示相结合：随机采样高维模型表示高斯过程回归（RS-HDMR-GPR）。使用高斯过程回归来表示组件函数可以进行非参数（无偏）表示，并且仅可以使用具有期望维数的函数工作，从而不必在耦合阶数上进行扩展。所有组件功能均由一组样本确定。通过拟合多原子分子的六维和15维势能面（PES）以及通过计算四原子分子的振动光谱来测试该方法。

更新日期：2020-09-18

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