Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1287-1300 |
| Seitenumfang | 14 |
| Fachzeitschrift | Computers, Materials and Continua |
| Jahrgang | 67 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 12 Jan. 2021 |
| Publikationsstatus | Veröffentlicht - 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
- Werkstoffwissenschaften (insg.)
- Biomaterialien
- Mathematik (insg.)
- Modellierung und Simulation
- Ingenieurwesen (insg.)
- Werkstoffmechanik
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: Computers, Materials and Continua, Jahrgang 67, Nr. 1, 2021, S. 1287-1300.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
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 -