scholarly journals Spatial Mapping of Electrostatics and Dynamics in Quantum Materials

2021 ◽  
Vol 27 (S1) ◽  
pp. 1436-1438
Author(s):  
Akshay Murthy ◽  
Stephanie Ribet ◽  
Roberto dos Reis ◽  
Vinayak Dravid
Keyword(s):  
Spatial Mapping ◽  
Quantum Materials

Role of landmark for spatial mapping in non-rigid environments

2003 ◽  
Author(s):  
C. G. L. Cao ◽  
S. L. Waxberg ◽  
E. Smith
Keyword(s):  
Spatial Mapping

SPATIAL MAPPING OF URANIUM IN GROUNDWATER AND RISK ASSESSMENT AROUND AN ATOMIC POWER STATION IN INDIA

2016 ◽  
Vol 15 (4) ◽  
pp. 783-790 ◽  
Author(s):  
Vinod Kumar Garg ◽  
Manbir Singh ◽  
Yogendra Prakash Gautam ◽  
Avinash Kumar
Keyword(s):  
Risk Assessment ◽  
Atomic Power Station ◽  
Spatial Mapping ◽  
Power Station

Spatial mapping and attribution of Wyoming wind turbines

Data Series ◽  
10.3133/ds524 ◽  
2010 ◽  
Author(s):  
Michael S. O'Donnell ◽  
Tammy S. Fancher
Keyword(s):  
Wind Turbines ◽  
Spatial Mapping

scholarly journals Advances in Rare‐Earth Tritelluride Quantum Materials: Structure, Properties, and Synthesis

2021 ◽  
pp. 2004762
Author(s):  
Kentaro Yumigeta ◽  
Ying Qin ◽  
Han Li ◽  
Mark Blei ◽  
Yashika Attarde ◽  
...  
Keyword(s):  
Rare Earth ◽  
Quantum Materials ◽  
Structure Properties

scholarly journals Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pankaj Rajak ◽  
Aravind Krishnamoorthy ◽  
Ankit Mishra ◽  
Rajiv Kalia ◽  
Aiichiro Nakano ◽  
...  
Keyword(s):  
Chemical Vapor Deposition ◽  
Reinforcement Learning ◽  
Vapor Deposition ◽  
Chemical Vapor ◽  
Time Behavior ◽  
Materials Synthesis ◽  
Design Synthesis ◽  
Learning Agent ◽  
Threshold Temperatures ◽  
Quantum Materials

AbstractPredictive 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.

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