Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes
Citations Over TimeTop 10% of 2017 papers
Abstract
Representation of multidimensional global potential energy surfaces suitable for spectral and dynamical calculations from high-level ab initio calculations remains a challenge. Here, we present a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression. Tests for the 3A″ state of SH2, which facilitates the SH + H ↔ S(3P) + H2 abstraction reaction and the SH + H' ↔ SH' + H exchange reaction, suggest that the Gaussian process is capable of providing a reasonable potential energy surface with a small number (∼1 × 102) of ab initio points, but it needs substantially more points (∼1 × 103) to converge reaction probabilities. The implications of these observations for construction of potential energy surfaces are discussed.
Related Papers
- → PACWON: A parallelizing compiler for workstations on a network(1998)
- Study and Two Types of Typical Usage of DataGrid Web Server Control(2005)
- Achieving Parameter of DBSCAN Based on Datagrid(2010)
- Using DataGrid Control to Realize DataBase of Querying in VB6.0(2000)
- Susquehanna Chorale Spring Concert "Roots and Wings"(2017)