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...

Full description

Bibliographic Details
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

id	doaj-677801e86c9c4479ada8ce35b66038ce
record_format	Article
spelling	doaj-677801e86c9c4479ada8ce35b66038ce2021-07-11T11:18:12ZengNature Publishing Groupnpj Computational Materials2057-39602021-07-01711810.1038/s41524-021-00572-yAutonomous reinforcement learning agent for stretchable kirigami design of 2D materialsPankaj Rajak0Beibei Wang1Ken-ichi Nomura2Ye Luo3Aiichiro Nakano4Rajiv Kalia5Priya Vashishta6Argonne Leadership Computing Facility, Argonne National LaboratoryCollaboratory for Advanced Computing and Simulations, University of Southern CaliforniaCollaboratory for Advanced Computing and Simulations, University of Southern CaliforniaArgonne Leadership Computing Facility, Argonne National LaboratoryCollaboratory for Advanced Computing and Simulations, University of Southern CaliforniaCollaboratory for Advanced Computing and Simulations, University of Southern CaliforniaCollaboratory for Advanced Computing and Simulations, University of Southern CaliforniaAbstract 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 is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2 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 MoS2 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.https://doi.org/10.1038/s41524-021-00572-y
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta
spellingShingle	Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials npj Computational Materials
author_facet	Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta
author_sort	Pankaj Rajak
title	Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
title_short	Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
title_full	Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
title_fullStr	Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
title_full_unstemmed	Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
title_sort	autonomous reinforcement learning agent for stretchable kirigami design of 2d materials
publisher	Nature Publishing Group
series	npj Computational Materials
issn	2057-3960
publishDate	2021-07-01
description	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 is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2 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 MoS2 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.
url	https://doi.org/10.1038/s41524-021-00572-y
work_keys_str_mv	AT pankajrajak autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT beibeiwang autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT kenichinomura autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT yeluo autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT aiichironakano autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT rajivkalia autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials AT priyavashishta autonomousreinforcementlearningagentforstretchablekirigamidesignof2dmaterials
_version_	1721309121238532096

Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials

Similar Items