Autonomous Discovery Projects

An important component of self-driving laboratories, dexterous, free-roaming robots — basically, robotic scientists – will free up researchers for more complex tasks.

Argonne developed a theoretical and AI framework to identify molecules with the desired energy storage properties from a large chemical space.

The ultimate goal of the project is to enable design of peptides with predictable functions. The capabilities we develop will be critical in advancing scientific discoveries in biology and other fields.

An autonomous laboratory based on continuous flow chemistry will improve productivity and enable design, development, synthesis, and scaleup of advanced materials.

Argonne is working to develop an autonomous method to identify the best stressor-specific, low-energy deconstruction pathways for conjugated polymers.

Argonne’s self-driving chemist platform executes an autonomous method for chemical discovery to improve chemical reactions and create new materials.

The Context-Aware Language Model for Science (CALMS) uses AI to answer technical questions, guide instrument operations and even control experiments conversationally, making advanced facilities accessible to more researchers.

Historically, scientist waited hours to align X-ray mirrors before each experiment. AutoFocus, a new AI system, does it in ten minutes, enabling researchers spend more time on discoveries.

Automated ​“design-build-test-learn” engine where AI invents new proteins, turns those ideas into lab-ready plans, and robots run the experiments and learn from the results to dramatically speed scientific discovery for critical biotechnology applications.