The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this work by using artificial neural network approach. Thepredictions of tensile strength of HFG sheets made by the proposed approach arecompared to those generated by molecular dynamics simulations. The resultsindicate that our proposed computing technique can be used as a powerful toolfor predicting the tensile strength of the HFG sheet.Nanyang Technological UniversityPublished versionVV and AG gratefully acknowledge the financial support in form of scholarship provided by the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
Abstract Developing accurate yet fast computational tools to simulate complex physical phenomena is ...
The matrix-reinforcement interface has been studied extensively to enhance the performance of polyme...
Machine learning has brought great convenience to material property prediction. However, most existi...
The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this wor...
The quality and performance of composite-based materials are functions of their mechanical propertie...
Graphene is a single-layer of carbon atoms forming hexagonal structures connected by sp2 bonds. It h...
The performance of graphene/epoxy nanocomposites strongly depends on the interfacial interaction bet...
The focus of this work is to develop the knowledge of prediction of the physical and chemical proper...
The focus of this work is to develop the knowledge of prediction of the physical and chemical proper...
Machine learning (ML) has been vastly used in various fields, but its application in engineering sci...
Materials science is of fundamental significance to science and technology because our industrial ba...
3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D...
Machine learning models were introduced to develop a relationship between the elemental composition ...
Penta-graphene (PG) is a 2D carbon allotrope composed of a layer of pentagons having sp2- and sp3- b...
<p>Graphene is a 2D carbon material that is impermeable to all gases. By engineering pores into grap...
Abstract Developing accurate yet fast computational tools to simulate complex physical phenomena is ...
The matrix-reinforcement interface has been studied extensively to enhance the performance of polyme...
Machine learning has brought great convenience to material property prediction. However, most existi...
The mechanical properties of hydrogen functionalized graphene (HFG) sheets werepredicted in this wor...
The quality and performance of composite-based materials are functions of their mechanical propertie...
Graphene is a single-layer of carbon atoms forming hexagonal structures connected by sp2 bonds. It h...
The performance of graphene/epoxy nanocomposites strongly depends on the interfacial interaction bet...
The focus of this work is to develop the knowledge of prediction of the physical and chemical proper...
The focus of this work is to develop the knowledge of prediction of the physical and chemical proper...
Machine learning (ML) has been vastly used in various fields, but its application in engineering sci...
Materials science is of fundamental significance to science and technology because our industrial ba...
3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D...
Machine learning models were introduced to develop a relationship between the elemental composition ...
Penta-graphene (PG) is a 2D carbon allotrope composed of a layer of pentagons having sp2- and sp3- b...
<p>Graphene is a 2D carbon material that is impermeable to all gases. By engineering pores into grap...
Abstract Developing accurate yet fast computational tools to simulate complex physical phenomena is ...
The matrix-reinforcement interface has been studied extensively to enhance the performance of polyme...
Machine learning has brought great convenience to material property prediction. However, most existi...