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Autonomous Reinforcement Learning Agent For Stretchable Kirigami Design of 2D Materials by Musa Ibne Mannan is a document available to read on EtoBox.

What is Autonomous Reinforcement Learning Agent For Stretchable Kirigami Design of 2D Materials about?

This article presents a reinforcement learning (RL) approach to optimize kirigami designs for 2D materials like MoS2, achieving stretchability over 45% with 6 cuts. The RL agent, trained on a small dataset from molecular dynamics simulations, can also predict highly stretchable structures with 8 and 10 cuts from a vast search space. The study highlights the potential of machine learning in material design, particularly for enhancing the mechanical properties of 2D materials through strategic cut patterns.

Author

Musa Ibne Mannan

Language

EN