Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for mat...
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Nature Portfolio
2021
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oai:doaj.org-article:d4144a8214a94720bed9ca615ef56f692021-12-02T15:33:10ZAutonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials10.1038/s41524-021-00535-32057-3960https://doaj.org/article/d4144a8214a94720bed9ca615ef56f692021-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00535-3https://doaj.org/toc/2057-3960Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.Pankaj RajakAravind KrishnamoorthyAnkit MishraRajiv KaliaAiichiro NakanoPriya VashishtaNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021) |
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DOAJ |
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DOAJ |
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EN |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Pankaj Rajak Aravind Krishnamoorthy Ankit Mishra Rajiv Kalia Aiichiro Nakano Priya Vashishta Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| description |
Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis. |
| format |
article |
| author |
Pankaj Rajak Aravind Krishnamoorthy Ankit Mishra Rajiv Kalia Aiichiro Nakano Priya Vashishta |
| author_facet |
Pankaj Rajak Aravind Krishnamoorthy Ankit Mishra Rajiv Kalia Aiichiro Nakano Priya Vashishta |
| author_sort |
Pankaj Rajak |
| title |
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| title_short |
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| title_full |
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| title_fullStr |
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| title_full_unstemmed |
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| title_sort |
autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/d4144a8214a94720bed9ca615ef56f69 |
| work_keys_str_mv |
AT pankajrajak autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials AT aravindkrishnamoorthy autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials AT ankitmishra autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials AT rajivkalia autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials AT aiichironakano autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials AT priyavashishta autonomousreinforcementlearningagentforchemicalvapordepositionsynthesisofquantummaterials |
| _version_ |
1718387111903625216 |