A neural network potential with rigorous treatment of long-range dispersion-Reference-Cited by-同舟云学术

A neural network potential with rigorous treatment of long-range dispersion

Published:2023 Issue:3 Volume:2 Page:718-727
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Tu Nguyen Thien Phuc¹^ORCID,Rezajooei Nazanin²^ORCID,Johnson Erin R.³^ORCID,Rowley Christopher N.¹^ORCID

Affiliation:

1. Carleton University, 1125 Colonel By Dr, Ottawa, ON, Canada

2. Interdisciplinary Program in Scientific Computing, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C 5S7, Newfoundland and Labrador, Canada

3. Department of Chemistry, Dalhousie University, 6274 Coburg Rd, Halifax, B3H 4R2, Nova Scotia, Canada

Abstract

MLXDM: Machine Learned eXchange-hole Dipole Moment dispersion correction for Neural Network Potentials.

Funder

Compute Canada

Natural Sciences and Engineering Research Council of Canada

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00150K

Reference61 articles.

1. Machine learning based interatomic potential for amorphous carbon

2. SchNet – A deep learning architecture for molecules and materials

3. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

4. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

5. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

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