Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
Abstract Mechanical behavior of 2D materials such as MoS2 can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties...
Main Authors: | Pankaj Rajak, Beibei Wang, Ken-ichi Nomura, Ye Luo, Aiichiro Nakano, Rajiv Kalia, Priya Vashishta |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-07-01
|
Series: | npj Computational Materials |
Online Access: | https://doi.org/10.1038/s41524-021-00572-y |
Similar Items
-
Nanoindentation on Monolayer MoS2 Kirigami
by: Beibei Wang, et al.
Published: (2019-06-01) -
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
by: Pankaj Rajak, et al.
Published: (2021-07-01) -
RXMD: A scalable reactive molecular dynamics simulator for optimized time-to-solution
by: Ken-ichi Nomura, et al.
Published: (2020-01-01) -
PND: Physics-informed neural-network software for molecular dynamics applications
by: Taufeq Mohammed Razakh, et al.
Published: (2021-07-01) -
Thermal conductivity of MoS2 monolayers from molecular dynamics simulations
by: Aravind Krishnamoorthy, et al.
Published: (2019-03-01)