Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage-Reference-Cited by-同舟云学术

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

Published:2024 Issue:14 Volume:12 Page:8502-8515
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Ojih Joshua¹,Al-Fahdi Mohammed¹^ORCID,Yao Yagang²,Hu Jianjun³^ORCID,Hu Ming¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of South Carolina, SC 29208, USA

2. National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

3. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

Abstract

Prediction of crystal structures with desirable material properties is a grand challenge in materials research. We deployed graph theory assisted structure searcher and combined with universal machine learning potentials to accelerate the process.

Funder

University of South Carolina

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D3TA06190F

Reference49 articles.

1. C. C.Pantelides , C. S.Adjiman and K.Andrei V , Prediction and Calculation of Crystal Structures , ed. S. A.-E. A. Aspuru-Guzik , Springer Cham , 2014 , pp. 25–58

2. H.Callen , Thermodynamics and an Introduction to Thermostatistics , John Wiley & Sons , 1985

3. B. C.Revard , W. W.Tipton and R. G.Hennig , Structure and Stability Prediction of Compound with Evolutionary Algorithm , 2014

4. MAGUS: machine learning and graph theory assisted universal structure searcher

5. The density functional formalism, its applications and prospects

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3. Correction: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage;Journal of Materials Chemistry A;2024