Predicting the electronic and structural properties of two-dimensional materials using machine learning

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Ehsan Alibagheri
  • Bohayra Mortazavi
  • Timon Rabczuk

Organisationseinheiten

Externe Organisationen

  • University of Tehran
  • Ton Duc Thang University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)1287-1300
Seitenumfang14
FachzeitschriftComputers, Materials and Continua
Jahrgang67
Ausgabenummer1
Frühes Online-Datum12 Jan. 2021
PublikationsstatusVeröffentlicht - 2021

Abstract

Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features. In this work, we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy. Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.

ASJC Scopus Sachgebiete

Zitieren

Predicting the electronic and structural properties of two-dimensional materials using machine learning. / Alibagheri, Ehsan; Mortazavi, Bohayra; Rabczuk, Timon.
in: Computers, Materials and Continua, Jahrgang 67, Nr. 1, 2021, S. 1287-1300.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Alibagheri E, Mortazavi B, Rabczuk T. Predicting the electronic and structural properties of two-dimensional materials using machine learning. Computers, Materials and Continua. 2021;67(1):1287-1300. Epub 2021 Jan 12. doi: 10.32604/cmc.2021.013564
Alibagheri, Ehsan ; Mortazavi, Bohayra ; Rabczuk, Timon. / Predicting the electronic and structural properties of two-dimensional materials using machine learning. in: Computers, Materials and Continua. 2021 ; Jahrgang 67, Nr. 1. S. 1287-1300.
Download
@article{8d8aa6b87be746618eb7203c2831dcc0,
title = "Predicting the electronic and structural properties of two-dimensional materials using machine learning",
abstract = "Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features. In this work, we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy. Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.",
keywords = "2D materials, Band gap, Gradient-boosted, Machine-learning",
author = "Ehsan Alibagheri and Bohayra Mortazavi and Timon Rabczuk",
note = "Funding Information: Acknowledgement: E. A. acknowledges the University of Tehran Research Council for support of this study.",
year = "2021",
doi = "10.32604/cmc.2021.013564",
language = "English",
volume = "67",
pages = "1287--1300",
journal = "Computers, Materials and Continua",
issn = "1546-2218",
publisher = "Tech Science Press",
number = "1",

}

Download

TY - JOUR

T1 - Predicting the electronic and structural properties of two-dimensional materials using machine learning

AU - Alibagheri, Ehsan

AU - Mortazavi, Bohayra

AU - Rabczuk, Timon

N1 - Funding Information: Acknowledgement: E. A. acknowledges the University of Tehran Research Council for support of this study.

PY - 2021

Y1 - 2021

N2 - Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features. In this work, we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy. Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.

AB - Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted (GB) ML algorithms are known to provide highly accurate predictions and have shown great potential to predict material properties based on the importance of features. In this work, we applied the GB algorithm to a dataset of electronic and structural properties of 2D materials in order to predict the specification with high accuracy. Conducted statistical analysis of the selected features identifies design guidelines for the discovery of novel 2D materials with desired properties.

KW - 2D materials

KW - Band gap

KW - Gradient-boosted

KW - Machine-learning

UR - http://www.scopus.com/inward/record.url?scp=85099392090&partnerID=8YFLogxK

U2 - 10.32604/cmc.2021.013564

DO - 10.32604/cmc.2021.013564

M3 - Article

AN - SCOPUS:85099392090

VL - 67

SP - 1287

EP - 1300

JO - Computers, Materials and Continua

JF - Computers, Materials and Continua

SN - 1546-2218

IS - 1

ER -