Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition-Reference-Cited by-同舟云学术

Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition

Published:2021 Issue:40 Volume:23 Page:23325-23335
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Roncaglia Cesare¹^ORCID,Rapetti Daniele¹,Ferrando Riccardo¹^ORCID

Affiliation:

1. Dipartimento di Fisica dell'Università di Genova, via Dodecaneso 33, Genova 16146, Italy

Abstract

The lowest-energy structures of AgCu nanoalloys are searched for by global optimization algorithms for sizes 100 and 200 atoms depending on composition, and their structures and mixing energy are analyzed by machine learning tools.

Funder

Università degli Studi di Genova

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/D1CP02143E

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1. A Highly Stable N-Heterocyclic Carbene Complex of Trichloro-oxo-vanadium(V) Displaying Novel Cl−Ccarbene Bonding Interactions

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4. Size-Dependent Shifts of Plasmon Resonance in Silver Nanoparticle Films Using Controlled Dissolution: Monitoring the Onset of Surface Screening Effects

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