Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
Abstract
Abstract Mechanical behavior of 2D materials such as MoS 2 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 is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS 2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS 2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; National Science Foundation (NSF); Aurora Early Science Program
- OSTI Identifier:
- 1806585
- Alternate Identifier(s):
- OSTI ID: 1863249
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Name: npj Computational Materials Journal Volume: 7 Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Rajak, Pankaj, Wang, Beibei, Nomura, Ken-ichi, Luo, Ye, Nakano, Aiichiro, Kalia, Rajiv, and Vashishta, Priya. Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials. United Kingdom: N. p., 2021.
Web. doi:10.1038/s41524-021-00572-y.
Rajak, Pankaj, Wang, Beibei, Nomura, Ken-ichi, Luo, Ye, Nakano, Aiichiro, Kalia, Rajiv, & Vashishta, Priya. Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials. United Kingdom. https://doi.org/10.1038/s41524-021-00572-y
Rajak, Pankaj, Wang, Beibei, Nomura, Ken-ichi, Luo, Ye, Nakano, Aiichiro, Kalia, Rajiv, and Vashishta, Priya. Fri .
"Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials". United Kingdom. https://doi.org/10.1038/s41524-021-00572-y.
@article{osti_1806585,
title = {Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials},
author = {Rajak, Pankaj and Wang, Beibei and Nomura, Ken-ichi and Luo, Ye and Nakano, Aiichiro and Kalia, Rajiv and Vashishta, Priya},
abstractNote = {Abstract Mechanical behavior of 2D materials such as MoS 2 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 is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS 2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS 2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.},
doi = {10.1038/s41524-021-00572-y},
journal = {npj Computational Materials},
number = 1,
volume = 7,
place = {United Kingdom},
year = {Fri Jul 09 00:00:00 EDT 2021},
month = {Fri Jul 09 00:00:00 EDT 2021}
}
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