en.Wedoany.com Reported - Argonne National Laboratory, in collaboration with Lawrence Berkeley National Laboratory, Brookhaven National Laboratory, Oak Ridge National Laboratory, and SLAC National Accelerator Laboratory, has officially launched a new system named SYNAPS-I. According to an official release from Argonne National Laboratory, the system is designed to process massive imaging data generated by large-scale scientific facilities such as synchrotron radiation beamlines in real time, compressing traditional analysis processes that take hours or days down to mere seconds.
SYNAPS-I connects artificial intelligence models directly with high-performance computing resources and instrument control systems, enabling analysis and interpretation at the moment data is generated. This allows researchers to adjust parameters in real time during experiments, focus on specific areas, or filter out irrelevant data. Alec Sandy, Deputy Division Director of the X-ray Science Division at Argonne National Laboratory, stated that with upgrades to facilities like Argonne's Advanced Photon Source and Berkeley Lab's Advanced Light Source, the application of ptychographic imaging is rapidly expanding. Transforming raw data into interpretable results in real time maximizes the value of the U.S. Department of Energy's investment in these facilities. Matthew Cherukara, a computational scientist at Argonne and leader of the SYNAPS-I team, noted that the system is not only an automation tool but is envisioned as a cognitive partner for scientists. It can generate hypotheses, detect subtle correlations, and drive the transformation of facilities towards intelligent, autonomous laboratories.
The system originated from a public-private partnership, involving not only the five national laboratories but also university researchers, leaders in the AI field, and industry innovators. The deployment of SYNAPS-I aligns with the direction of the Department of Energy's "Genesis Mission" initiative, which aims to build a platform integrating data, computing, and advanced models to accelerate breakthroughs across multiple scientific fields. Project leader Irene Lo emphasized that the system provides rapid analysis methods delivered synchronously at the speed of data generation.
In traditional experimental workflows, delays exist between data capture, storage, and post-analysis, making it difficult for researchers to react immediately to unexpected data within the experimental window. SYNAPS-I addresses this by creating a continuous cycle of data inflow, interpretation, and feedback, shifting experiments from fixed, pre-set modes to adaptive operations. This provides a technical pathway for realizing the concept of autonomous laboratories. Relevant technical details and future application expansions will be continuously disclosed by the participating laboratories.
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