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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Chemical Engineering

Background:

  • Atomic Layer Deposition (ALD) is a crucial technique for thin-film material synthesis.
  • Autonomous control of ALD processes can accelerate materials discovery and optimization.
  • Integrating AI with hardware interfaces presents challenges in real-time control and performance.

Purpose of the Study:

  • To design and implement an AI interface for an ALD reactor enabling autonomous materials synthesis.
  • To evaluate the performance of AI agents in translating user queries into executable ALD processes.
  • To assess the impact of the AI interface on ALD control software overhead and agent capabilities.

Main Methods:

  • Developed a modular Python interface for ALD hardware control via TCP.
  • Integrated a simple AI agent leveraging a large language model (LLM) for process generation.
  • Encoded ALD processes using a JavaScript Object Notation (JSON) schema.
  • Evaluated AI agent performance on instruction and process discovery tasks with various LLMs.

Main Results:

  • The AI interface introduced minimal overhead (10 ms scale) to the ALD control software.
  • Most advanced AI models performed well on basic instruction tasks.
  • Only recent LLMs (o1, o3, GPT-5, Claude Opus 4) showed strong performance in process discovery.
  • Significant response variability was observed for complex challenges.

Conclusions:

  • The AI interface facilitates autonomous materials synthesis via ALD.
  • Current AI agents show potential for controlling ALD processes, particularly with advanced models.
  • Further AI research is needed to enhance agent performance in complex materials discovery tasks and reduce response variability.