Representing Potential Energy Surfaces with Neural Networks and High Dimensional Model Representations

被引：0

作者：

Manzhos, Sergei ^{[1
]}

Carrington, Tucker ^{[1
]}

机构：

[1] Queens Univ, Dept Chem, Kingston, ON K7L 3N6, Canada

来源：

INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009 (ICCMSE 2009) | 2012年 / 1504卷

关键词：

Potential Energy Surfaces; Dimensionality Reduction; High Dimensional Model Representation; Neural Networks;

D O I：

10.1063/1.4771811

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

I shall discuss a new method for fitting a potential function (or in fact any function) to multidimensional data. The algorithm uses a representation in terms of lower-dimensional component functions of optimized coordinates. It permits dimensionality reduction. Neural networks are used to construct the component functions.

引用

页码：785 / 787

页数：3

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