About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Predictive Synthesis of Quantum Materials with Offline Reinforcement Learning |
Author(s) |
Pankaj Rajak, Aravind Krishnamoorthy, Aiichiro Nakano, Rajiv Kalia, Priya Vashistha |
On-Site Speaker (Planned) |
Aravind Krishnamoorthy |
Abstract Scope |
Finding strategies to synthesize novel functional materials and structures is one of the central endeavors of chemical and materials science. Optimal material design involves a high dimensional parameter space search and time dependent sequential decision-making process, which requires development of new machine learning techniques that are capable of implicit decision-making over long period of times with minimal human supervision. Here, we have developed an autonomous agent using offline reinforcement learning for the predictive synthesis of a prototypical quantum material, monolayer MoS2, via chemical vapor deposition. After training, the RL agent successfully learns optimal synthesis policies in terms of threshold temperatures and chemical potentials for the onset of chemical reactions and provides mechanistic insight to predict new synthesis schedules for well-sulfidized, crystalline and phase pure MoS2 in minimum time, which is validated by molecular dynamics and experimental synthesis. |
Proceedings Inclusion? |
Undecided |