DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers-Reference-Cited by-同舟云学术

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers

Published:2021 Issue:38 Volume:23 Page:21995-22003
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Modee Rohit¹^ORCID,Agarwal Sheena²³,Verma Ashwini²,Joshi Kavita²³^ORCID,Priyakumar U. Deva¹^ORCID

Affiliation:

1. Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India

2. Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr Homi Bhabha Road, Pune-411008, India

3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh-201002, India

Abstract

We introduce a simple topological atomic descriptor, TAD, and a deep learning enabled topological interaction model (DART) for predicting energies of metal clusters for efficient identification of unique clusters.

Funder

Council of Scientific and Industrial Research, India

Tata Consultancy Services

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D1CP02956H

Reference63 articles.

1. Clusters: A bridge across the disciplines of physics and chemistry