Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression

Jonathan P. Alborzpour, David P. Tew, Scott Habershon

Research output: Contribution to journalArticle (Academic Journal)peer-review

35 Citations (Scopus)
281 Downloads (Pure)

Abstract

Solution of the time-dependent Schrödinger equation using a linear combination of basis functions, such as Gaussian wavepackets (GWPs), requires costly evaluation of integrals over the entire potential energy surface (PES) of the system. The standard approach, motivated by computational tractability for direct dynamics, is to approximate the PES with a second order Taylor expansion, for example centred at each GWP. In this article, we propose an alternative method for approximating PES matrix elements based on PES interpolation using Gaussian process regression (GPR). Our GPR scheme requires only single-point evaluations of the PES at a limited number of configurations in each time-step; the necessity of performing often-expensive evaluations of the Hessian matrix is completely avoided. In applications to 2-, 5-, and 10-dimensional benchmark models describing a tunnelling coordinate coupled non-linearly to a set of harmonic oscillators, we find that our GPR method results in PES matrix elements for which the average error is, in the best case, two orders-of-magnitude smaller and, in the worst case, directly comparable to that determined by any other Taylor expansion method, without requiring additional PES evaluations or Hessian matrices. Given the computational simplicity of GPR, as well as the opportunities for further refinement of the procedure highlighted herein, we argue that our GPR methodology should replace methods for evaluating PES matrix elements using Taylor expansions in quantum dynamics simulations.

Original languageEnglish
Article number174112
Number of pages13
JournalJournal of Chemical Physics
Volume145
Issue number17
Early online date1 Nov 2016
DOIs
Publication statusPublished - 7 Nov 2016

Fingerprint Dive into the research topics of 'Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression'. Together they form a unique fingerprint.

Cite this