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...

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Autores principales:	Pankaj Rajak, Beibei Wang, Ken-ichi Nomura, Ye Luo, Aiichiro Nakano, Rajiv Kalia, Priya Vashishta
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/677801e86c9c4479ada8ce35b66038ce
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spelling	oai:doaj.org-article:677801e86c9c4479ada8ce35b66038ce2021-12-02T16:14:45ZAutonomous reinforcement learning agent for stretchable kirigami design of 2D materials10.1038/s41524-021-00572-y2057-3960https://doaj.org/article/677801e86c9c4479ada8ce35b66038ce2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00572-yhttps://doaj.org/toc/2057-3960Abstract 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.Pankaj RajakBeibei WangKen-ichi NomuraYe LuoAiichiro NakanoRajiv KaliaPriya VashishtaNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 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
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.
format	article
author	Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo Aiichiro Nakano Rajiv Kalia Priya Vashishta
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 Portfolio
publishDate	2021
url	https://doaj.org/article/677801e86c9c4479ada8ce35b66038ce
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
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Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials

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