Prediction of sodium binding energy on 2D VS2via machine learning: a robust accompanying method to ab initio random structure searching-Reference-Cited by-同舟云学术

Prediction of sodium binding energy on 2D VS₂via machine learning: a robust accompanying method to ab initio random structure searching

Published:2023 Issue:21 Volume:25 Page:15008-15014
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Putungan Darwin B.¹^ORCID,Su Shaosen²,Gao Liang²,Goyal Ankit³,Lin Shi-Hsin⁴,Garg Akhil²^ORCID

Affiliation:

1. Physics Division, Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Philippines

2. State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

3. Institute of Physics, University of Amsterdam, Science Park 904, The Netherlands

4. Department of Materials and Optoelectronic Science, Center of Crystal Research, Research Center for Physical Properties and Microstructure of Metals, National Sun Yat-Sen University, Kaohsiung, Taiwan

Abstract

In this work, we employed the back-propagation neural network (BPNN) in predicting the energetics of different sodium adsorption phases on monolayer VS2 generated via ab initio random structure searching (AIRSS).

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP01043K

Reference37 articles.

1. Metallic VS2 Monolayer Polytypes as Potential Sodium-Ion Battery Anode via ab Initio Random Structure Searching

2. High-Pressure Phases of Silane

3. Structures at high pressure from random searching

4. High-pressure ionic and molecular phases of ammonia within density functional theory

5. Ab initiorandom structure search for 13-atom clusters of fcc elements

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