Machine Learning Optimization of the Collocation Point Set for Solving the Kohn

Machine Learning Optimization of the Collocation Point Set for Solving the Kohn–Sham Equation

Published:2019-11-14 Issue:49 Volume:123 Page:10631-10642
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Ku Jonas¹,Kamath Aditya¹,Carrington Tucker²^ORCID,Manzhos Sergei³^ORCID

Affiliation:

1. Department of Mechanical Engineering, National University of Singapore, Block EA #07-08, 9 Engineering Drive 1, Singapore 117576, Singapore

2. Chemistry Department, Queen’s University, Kingston, Ontario K7L 3N6, Canada

3. Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, 1650, boulevard Lionel-Boulet, Varennes QC J3X 1S2, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Strategic India Initiative by the National University of Singapore

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.9b09732

Reference72 articles.

1. Using an internal coordinate Gaussian basis and a space-fixed Cartesian coordinate kinetic energy operator to compute a vibrational spectrum with rectangular collocation

2. Using a neural network based method to solve the vibrational Schrödinger equation for H2O

3. On the advantages of a rectangular matrix collocation equation for computing vibrational spectra from small basis sets

4. Computing the Anharmonic Vibrational Spectrum of UF6 in 15 Dimensions with an Optimized Basis Set and Rectangular Collocation

5. A multimode-like scheme for selecting the centers of Gaussian basis functions when computing vibrational spectra

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