In an autonomous chemistry laboratory on Oak Ridge National Laboratory’s main campus, a mobile robot shuttles between workstations, most of which have their own robots. Guided by advanced artificial intelligence, the equipment — much of it custom-built — grabs chemicals and mixes them together. Sometimes it heats the mixtures in a furnace; at other times, it runs them through high-speed mixers. In either case, the new products are then sent to separate instruments for analysis. Along the way, computers automatically collect all the information created in the process — information on what worked as well as what didn’t — which will be used to accelerate scientific understanding and to improve AI models. A quarter mile across ORNL’s main campus, a state-of-the-art electron microscope examines a tiny sliver of an ultrathin material such as graphene. Because the microscope is highly susceptible both to vibrations and to electric and magnetic fields, the AI-enabled computers that control it are located in a separate building. Control algorithms tell the microscope to capture images of the 3-millimeter disk — about the width of two stacked pennies — and then zoom in on one or more areas that promise to yield useful information. These close-up images reveal intricate lattices of individual atoms, providing insight into the structure of the material, including atomic bonding and surface defects. 2 As with the autonomous chemistry lab, the computers collect and analyze a rising mountain of data while helping to manage the hundreds of gigabytes collected each day. About 20 miles down the road at the lab’s Manufacturing Demonstration Facility, high-powered computers use simulation to control a 3D printer producing metal parts. As the part is printed, infrared cameras and thermocouples autonomously monitor the surface to check if each point heats and cools as expected. These observations are fed into simulations run on ORNL’s Open Research Cloud, which can revise the print instructions on the fly to ensure that the part has the right combination of strength and other properties while being free of stresses that might spell trouble in the future. To more closely examine any stresses that do form, an additive manufacturing printer is taken to the Spallation Neutron Source — back at the main ORNL campus — and loaded directly into a beam line, where it is bombarded with neutrons while printing a 3D part. This allows researchers to look deep inside the part during the print job to see stresses at the atomic level as they form. Networking + AI + unique capabilities = groundbreaking research These autonomous laboratory projects and others across ORNL are connected to form the lab’s Interconnected Science Ecosystem — a labwide initiative launched in 2022 that aims to reimagine the way scientific research is performed. The initiative, commonly referred to as INTERSECT, has two primary goals. 2 3 The first is to use AI, robotics, computer simulation, the lab’s networking expertise and other high-tech tools to reduce the time it takes to conduct research, taking experiments that typically take days or even years and completing them in minutes. The second, more ambitious goal is to use these tools to deliver scientific breakthroughs that would be unachievable in traditional research settings. “The vision of INTERSECT is to create a network of autonomous, self-driving smart laboratories that enable new multidomain scientific research,” said Ben Mintz, one of the initiative’s two co-directors. Along the way, INTERSECT is streamlining the process of doing science by automating repetitive tasks and creating a standard set of software tools that can be used and adapted for seemingly dissimilar research endeavors. If it’s real, it’s reproducible On a practical level, the new processes are making the day-to-day business of conducting research both less tedious and more dependable. 4 For a working researcher, it’s not enough to do an experiment and get a result. You need to be able to repeat it and get the same result. In the words of INTERSECT’s other director, Rob Moore, “If it’s real, it’s reproducible.” “Because of that, we end up doing a lot of things over and over and over again,” Moore said. “We’ll run experiments with slightly different parameters to figure out what’s real and what’s not. We’ll run theoretical calculations with slightly different parameters to see what’s reliable and what’s not.” The repetition is critical, but it can also be mindnumbingly dull. It’s also a process that hasn’t changed for many decades. “Chemistry labs haven’t gone through much of a revolution,” said Sheng Dai, who leads the autonomous chemistry lab. “Current labs are the same to some extent as we had 40 years ago. Our overarching goal is to develop a more smart lab. “There’s a lot of advantage to doing this. Number one, we can do an experiment very safely; there’s no human error. And number two, the experiment can be done efficiently, because the robots can work 24/7 — and in the dark.”