ChemOS

ChemOS coordinates autonomous laboratory operations by integrating automated equipment with computational methods to enable autonomous experimentation and support materials discovery.

Key Features:

• Modular Design: Structured into layers essential for deployment and operation of self-driving laboratories, allowing customization and scaling of laboratory operations.
• Remote Control Capabilities: Supports remote access for management of laboratory processes from distant locations.
• Distributed Computing Integration: Provides connectivity to distributed computing resources for complex data processing tasks.
• Machine Learning Methods: Incorporates machine learning techniques to enable intelligent automation, autonomous discovery, and experimentation.
• Variable Autonomy Levels: Operates across a spectrum of autonomy from fully unsupervised experiments to workflows that incorporate researcher inputs and feedback.

Scientific Applications:

• Automated equipment execution: Demonstrated execution on various automated equipment across five distinct applications.
• Data acquisition and analysis: Handles data acquisition and analysis in automated experiments.
• Experiment management and optimization: Manages experiment workflows and performs optimization within autonomous loops.
• Materials discovery and development: Facilitates autonomous experimentation to reduce time and resources for materials discovery and development.

Methodology:

Integrates automated equipment with advanced computational tools to enable seamless communication between hardware components and software layers. Leverages machine learning algorithms to analyze experimental data in real-time and make informed decisions that drive the autonomous experimentation process.