Robotics have long been used in manufacturing to automate extremely complex processes reliably and rapidly. But those systems were designed to perform specific, repetitive tasks in factories that were built to accommodate robots.

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.

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

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

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

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

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

There is an urgent need to develop a data infrastructure comprising autonomous synthesis and characterization platforms that can manage and analyze the heterogeneous datasets that will be produced at massive volumes and rapid rates.