MACHINE LEARNING ACCELERATED ATOMISTIC SIMULATIONS FOR 2D MATERIALS WITH DEFECTS

Keyphrases

• Atomistic Simulation 100%
• 2D Materials 100%
• Machine Learning 100%
• Machine Learning Potential 100%
• Artificial Neural Network 75%
• Physical Properties 25%
• Atomic Structure 25%
• Molecular Dynamics Simulation 25%
• Unique Properties 25%
• Ab Initio Calculations 25%
• Empirical Potentials 25%
• Graphene 25%
• Material System 25%
• Density Functional Theory 25%
• Density Functional Calculations 25%
• Interatomic Potential 25%
• Chemical Properties 25%
• First-principles Calculations 25%
• Structure-property Relationships 25%
• Structural Defects 25%
• Energy Storage 25%
• Cutting-edge Technology 25%
• Defect Effect 25%
• Promising Applications 25%
• Surface Mounting 25%
• High Computational Cost 25%
• Complex Materials 25%
• Potential Energy Surface 25%
• High Reliability 25%
• Defective Graphene 25%
• 3D Materials 25%
• Aerospace 25%
• Invariant Representation 25%
• Machine Learning Based 25%
• High-fidelity Simulation 25%
• Two-hidden-layer 25%
• LAMMPS 25%
• Technology Area 25%

Material Science

• Two-Dimensional Material 100%
• Density 50%
• Graphene 50%
• Atomic Structure 25%
• Chemical Property 25%
• Ab Initio Calculation 25%
• Surface Energy 25%
• Physical Property 25%