Scientists at Argonne National Laboratory and the University of Illinois Chicago have developed digital twin software to optimize the use of Department of Energy (DOE) facilities. The software, built with artificial intelligence and machine learning, simulates the research environments that scientists encounter at DOE facilities. This tool aims to help the thousands of scientists who use these facilities make the best possible use of their limited time.
The DOE’s Office of Science supports world-class facilities that enable scientists to image and characterize materials at the atomic scale and track particle movement at incredibly fast speeds. However, instrument time at these facilities is limited and can range from a few hours to several days. If an experiment fails, scientists must wait for another opportunity, which could take up to a year due to the lengthy proposal process.
The digital twin software created by Subramanian Sankaranarayanan and his team allows researchers to simulate experiments under different conditions, such as sample type, temperature, and pressure. By viewing the results instantaneously, scientists can assess the behavior of atoms and molecules in a sample over time or distance. They can then adjust the conditions, run a new simulation, and compare the results. The software also allows scientists to test different theoretical methods for creating simulations.
The aim of the digital twin software is to provide scientists with a deeper understanding of the expected results of their experiments and to optimize their time at DOE facilities. By being better informed about the impact of various experimental conditions, researchers can maximize the efficiency and productivity of their allotted time. Even after completing experiments, scientists can return to the digital twin software to further analyze and refine their data.
Select users are currently testing the software with several analysis methods available at DOE facilities. The team plans to enhance the software and expand its capabilities to support other analysis methods in the future. This digital twin tool has the potential to significantly improve the productivity of scientists accessing DOE user facilities.
In addition to the digital twin software, scientists are using artificial intelligence, machine learning, and supercomputers to transform the future of research. The DOE Office of Basic Energy Sciences is funding this research, which aligns with their goal of advancing scientific understanding and innovation.
Overall, the digital twin software developed by Argonne and the University of Illinois Chicago holds promise for revolutionizing research by enabling scientists to make optimal use of DOE facilities and their limited time. In an era where time is precious, this tool has the potential to accelerate scientific discoveries and drive innovation in various fields of study.
