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...
Enregistré dans:
Auteurs principaux: | Pankaj Rajak, Beibei Wang, Ken-ichi Nomura, Ye Luo, Aiichiro Nakano, Rajiv Kalia, Priya Vashishta |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/677801e86c9c4479ada8ce35b66038ce |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
par: Pankaj Rajak, et autres
Publié: (2021) -
Multipolicy Robot-Following Model Based on Reinforcement Learning
par: Ning Yu, et autres
Publié: (2021) -
Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
par: Pikee Priya, et autres
Publié: (2021) -
Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction
par: Ting Lu, et autres
Publié: (2021) -
A study of real-world micrograph data quality and machine learning model robustness
par: Xiaoting Zhong, et autres
Publié: (2021)