TY - JOUR
T1 - Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computation
AU - Zhang, Zesheng
AU - Hong, Yang
AU - Hou, Bo
AU - Zhang, Zhongtao
AU - Negahban, Mehrdad
AU - Zhang, Jingchao
N1 - Funding Information:
This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative, USA .
Funding Information:
This work was completed utilizing the Holland Computing Center of the University of Nebraska, which receives support from the Nebraska Research Initiative, USA.
Publisher Copyright:
© 2019
PY - 2019/7
Y1 - 2019/7
N2 - Machine learning (ML) has been vastly used in various fields, but its application in engineering science remains in infancy. In this work, for the first time, different machine learning algorithms and artificial neural network (ANN) structures are used to predict the mechanical properties of single-layer graphene under various impact factors of system temperature, strain rate, vacancy defect and chirality. The predictions include fracture strain, fracture strength and Young's modulus. High throughput computation (HTC) combined with classical molecular dynamics (MD) simulation is used to generate the training dataset for the ML models. It was discovered that both temperature and vacancy defect have negative effects on the predicted properties while strain rate has positive correlations with the prediction results. The stochastic gradient descent (SGD) method could not properly capture the effects of the different impact factors on the mechanical properties of graphene, while k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT) and ANN provided desirable prediction results. Discoveries in this work provide new perspectives on the study of mechanical properties using state-of-the-art computational methods.
AB - Machine learning (ML) has been vastly used in various fields, but its application in engineering science remains in infancy. In this work, for the first time, different machine learning algorithms and artificial neural network (ANN) structures are used to predict the mechanical properties of single-layer graphene under various impact factors of system temperature, strain rate, vacancy defect and chirality. The predictions include fracture strain, fracture strength and Young's modulus. High throughput computation (HTC) combined with classical molecular dynamics (MD) simulation is used to generate the training dataset for the ML models. It was discovered that both temperature and vacancy defect have negative effects on the predicted properties while strain rate has positive correlations with the prediction results. The stochastic gradient descent (SGD) method could not properly capture the effects of the different impact factors on the mechanical properties of graphene, while k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT) and ANN provided desirable prediction results. Discoveries in this work provide new perspectives on the study of mechanical properties using state-of-the-art computational methods.
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U2 - 10.1016/j.carbon.2019.03.046
DO - 10.1016/j.carbon.2019.03.046
M3 - Article
AN - SCOPUS:85063544575
SN - 0008-6223
VL - 148
SP - 115
EP - 123
JO - Carbon
JF - Carbon
ER -