Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization.

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Synchrotron radiation is crucial for nanomaterial diagnostics, determining electronic and atomic structure.
• High costs and experimental planning challenges limit synchrotron research accessibility.
• Deep reinforcement learning (DRL) offers potential but requires a robust simulation environment.

Purpose of the Study:

• To develop a digital modeling approach for complex multiscale physicochemical environments in nanocatalyst diagnostics.
• To create a reliable training environment for DRL agents in experimental control.
• To optimize experimental strategies and reduce resource expenditure in synchrotron-based research.

Main Methods:

• Decomposition of the experimental system into physically plausible nodes.
• Merging and optimization of nodes into a metagraphic representation.
• Utilizing the metagraphic model for state prediction and experimental parameter optimization.

Main Results:

• A unified digital model for complex multiscale physicochemical environments was established.
• The metagraphic model enables direct prediction of system states and optimization of experimental conditions.
• The model serves as a training environment for DRL agents, facilitating strategy optimization.

Conclusions:

• The proposed metagraphic approach effectively models complex experimental environments for nanomaterial diagnostics.
• This method enhances the efficiency and reduces the cost of synchrotron radiation experiments.
• The approach facilitates the development of advanced DRL-based control strategies for scientific research.