TY - GEN A1 - Völker, Christoph T1 - WEBSLAMD N2 - The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future. 1. Summary Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact. Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution. Workflow Digital Lab Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations. Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization. AI-Optimization Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements. KW - Materials informatics KW - Scientific software KW - Sequential learning PY - 2022 UR - https://github.com/BAMresearch/WEBSLAMD U6 - https://doi.org/10.26272/opus4-56640 PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56640 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -