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
收藏
页码：785 / 787
页数：3
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