Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics

被引：2

作者：

Reiter, Sebastian ^{[1
]}

Schnappinger, Thomas ^{[1
]}

de Vivie-Riedle, Regina ^{[1
]}

机构：

[1] Ludwig Maximilians Univ Munchen, Dept Chem, Butenandtstr 5-13, Munich, Germany

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS | 2019年 / 11731卷

关键词：

Quantum dynamics; Machine learning; Autoencoder; Dimensionality reduction;

D O I：

10.1007/978-3-030-30493-5_73

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A key step in performing quantum dynamics for a chemical system is the reduction of dimensionality to allow a numerical treatment. Here, we introduce a machine learning approach for the (semi)automatic construction of reactive coordinates. After generating a meaningful data set from trajectory calculations, we train an autoencoder to find a low-dimensional set of non-linear coordinates for use in molecular quantum dynamics. We compare the wave packet dynamics of proton transfer reactions in both linear and non-linear coordinate spaces and find significant improvement for physical properties like reaction timescales.

引用

页码：783 / 787

页数：5

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