So DSL scientists have undertaken a project to develop a lab service for interactive, scalable, reproducible data science, leveraging machine learning methods to reduce simulation costs and increase data quality and value for researchers.Īs a testbed for developing this service, project leaders collaborated with the Argonne Leadership Computing Facility (ALCF), Materials Data Facility (MDF) team in Argonne’s Physical Sciences and Engineering Division, and with the Globus team, in a project leveraging machine learning to improve model development.Ĭhallenge: Computing stopping power in materials science The more easily this data can be analyzed by various analysis services, the more useful it is for the researchers who rely on it to drive discovery. Argonne projects produce data of great scientific value, from explorations into renewable energy and global climate change, to cutting-edge research to bolster the fight against cancer. The Data Science and Learning Division (DSL) at Argonne National Laboratory tackles advanced scientific problems where data analysis and artificial intelligence can provide critical insights and accelerate discovery.Ī primary thrust of the division is to build cross-cutting capability at Argonne to tackle advanced scientific problems where data analysis and artificial intelligence (AI) are key problem solving strategies.
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