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

Journal Article · 14 July 2021 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-021-00535-3 · OSTI ID:1807979

Rajak, Pankaj ; Krishnamoorthy, Aravind ; Mishra, Ankit; Kalia, Rajiv; Nakano, Aiichiro ; Vashishta, Priya

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 MoS ₂ 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 MoS ₂ . 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: NONE; SC0014607
• OSTI ID: 1807979
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 7; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English