{"id":7350,"date":"2025-01-28T14:19:35","date_gmt":"2025-01-28T13:19:35","guid":{"rendered":"https:\/\/blog.rwth-aachen.de\/itc-events\/?post_type=tribe_events&p=7350"},"modified":"2025-01-28T14:19:35","modified_gmt":"2025-01-28T13:19:35","slug":"nhr4ces-materials-science-with-advanced-data-management-and-data-science-techniques","status":"publish","type":"tribe_events","link":"https:\/\/blog.rwth-aachen.de\/itc-events\/en\/event\/nhr4ces-materials-science-with-advanced-data-management-and-data-science-techniques\/","title":{"rendered":"NHR4CES – Materials Science with Advanced Data Management and Data Science Techniques"},"content":{"rendered":"

\"Logo<\/a>Name:<\/strong> Materials Science with Advanced Data Management and Data Science Techniques<\/p>\n

Date: <\/strong>May 07-08, 2025<\/p>\n

Format: <\/b>Online<\/p>\n

More Information & Register<\/strong><\/a><\/p>\n

\u00a0<\/strong><\/p>\n

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Description<\/span><\/h3>\n
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We invite you to our NHR4CES Community Workshop 2025! This year\u2019s topic is Materials Science with Advanced Data Management and Data Science Techniques. The online workshop is organized by SDL Materials Design, CSG Data Management and CSG Data Science and Machine Learning. The workshop will take place on May 07, 1pm-5pm and May 08, 9am-1pm.<\/p>\n

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With the increasing computational power, an abundance of data has become available in different research fields. To handle such an ever increasing amount of data, tools such as data management, machine learning and\/or workflow managers receive increasing attention. In the field of materials science such approaches allow to investigate a wide range of materials and their properties in a systematic manner. The workflow managers are used in high-throughput calculations leading to the generation of huge amount of data. Although, high-throughput calculations are limited by the structure size. Structures with >10^3 atoms are limited by the computational resources. Researchers use the generated data and\/or intelligent data mining as an input to machine learning techniques to investigate material properties, beyond limitations imposed by computational resources.<\/p>\n<\/div>\n

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Additionally, through the collection of rich metadata in all steps of the workflow, results can be easily reproduced and reused by other researchers. This workshop is going to cover major parts of a data life cycle from generation of data via automatized workflow, their collection using data manager solutions, and their processing and\/or re-using via application of machine learning while following the FAIR data principles.<\/p>\n<\/div>\n

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<\/p>","protected":false},"excerpt":{"rendered":"

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