Category: Core Projects

The MEDAL project, which stands for Mobilizing the Emerging Diverse AI Talent, brings together expertise in large language models, virtual reality, and robotic automation to produce collegiate-level coursework focused on using these tools to design and establish automated scientific laboratories. 

Project Leaders:

Dr. Sumit Kumar Jha – Lead PI, University of Texas San Antonio 

Dr. Arvind Ramanathan – ANL Partner and Co-PI, Argonne National Laboratory 

Dr. Sreenivasan Ramamurthy – Co-PI, Bowie State University 

Dr. Sunny Raj – Co-PI, Oakland University  

Dr. Sathish Kumar – Co-PI, Cleveland State University  

Dr. Giri Narasimhan – Co-PI, Florida International University 

Dr. Rickard Ewetz – Co-PI, University of Central Florida 

This project aims to modernize the field of medical isotope production by developing fully automated, robotically driven workflows to minimize radiation exposure for staff. The primary objective is to advance mobile robotics capable of reducing hands-on radiation risks by enabling precise, autonomous navigation and task execution. The project focuses on achieving reliable mobility for a two-arm robotic system, including precision docking at stationary workstations and the coordinated use of both robotic arms for complex tasks. By integrating advanced sensing and low-latency feedback, the system autonomously transports materials between workstations, establishing a foundation for future funding opportunities in autonomous labs and isotope production automation. Project in partnership with Physical Sciences and Engineering.

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Polybot is an AI-driven self-driving laboratory revolutionizing the inverse design of functional polymers through automation and robotics. By integrating robotic platforms such as Chemspeed, UR5e, and Tecan with RPL’s Workflow Execution Interface (WEI), Polybot autonomously handles tasks ranging from monomer recipe formulation to polymer synthesis, purification, and characterization. WEI’s Python-based tool coordinates complex workflows using ROS and TCP sockets, enabling seamless communication between robots for synchronized operations. Polybot’s physics-informed ML model accurately predicts electrochromic polymer properties, refining itself through active learning, and achieving high-precision results in just a few iterations. With this streamlined, fully automated workflow and open-access ECP informatics database, Polybot paves the way for collaborative, high-throughput polymer research and AI-driven material discovery. Project in partnership with Physical Sciences and Engineering and Center for Nanoscale Materials.

The RPL has developed a groundbreaking method that leverages robotics to automate the preparation, characterization, and disposal of complex liquid samples for synchrotron coherent X-ray scattering experiments. Using a pendant drop technique shielded from air turbulence, the automated experiments achieved results consistent with traditional containers, making this approach suitable for high-precision studies. The robotic system, featuring an electronic pipette mounted on a robotic arm, enables precise sample handling and high-throughput exchange. By integrating a single Python script with beamline and robot control libraries, this enables seamless automation, paving the way for AI-driven, fully autonomous material design at large-scale scientific facilities. This approach enhances experimental efficiency and consistency, revolutionizing the study of complex fluids. In partnership with the Advanced Photon Source.

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The RPL introduced a compact, fully automated robotic system designed for the precise assembly of small liquid/gel chambers with advanced robotics at its core. This system automates the entire process, from sample preparation to data collection, for X-ray and neutron scattering experiments. Featuring transparent polycarbonate windows and a metallic body for temperature control, it minimizes human interference, improving reliability, particularly for delicate biological samples. Its seamless integration into experimental stations like synchrotrons and X-ray free electron lasers enables efficient, automated sample exchange during beamtime, while AI-driven data analysis ensures a fully autonomous workflow. This robotic platform revolutionizes soft material discovery and biomaterial development through advanced automation and robotics. In partnership with the Advanced Photon Source.

Automating the design of antimicrobial peptides, which can be used to combat antimicrobial-resistant bacterial strains, was the key motivation for developing Argonne’s BIO-Workcell. This proof of concept was demonstrated on E. coli bacterial strains using high-throughput screening techniques in concert with state-of-the-art artificial intelligence. This approach shows promise for accelerating the drug discovery process, making it faster and more efficient to identify potential therapeutic compounds. In partnership with Biosciences.