Construction of potential energy surfaces for polyatomic molecules with Gaussian Process Regression:

来源 :第十五届全国化学动力学会议 | 被引量 : 0次 | 上传用户：rgzgjh

【摘要】

：

Gaussian Process(GP)Regression is an efficient non-parametric method for constructing multi-dimensionalpotential energy surfaces(PESs)for polyatomic molecules.

【作者】

：

Yafu Guan Shuo Yang Dong H.Zhang

【机构】

：

State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemist

【出处】

：

第十五届全国化学动力学会议

【发表日期】

：

2017年10期

【关键词】

：

Potential energy surface Gaussian Process Regression active learning

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　　Gaussian Process(GP)Regression is an efficient non-parametric method for constructing multi-dimensionalpotential energy surfaces(PESs)for polyatomic molecules.

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Construction of potential energy surfaces for polyatomic molecules with Gaussian Process Regression:

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