| Abstract Scope |
The MatCHMaker project aims to reduce time, costs, and uncertainties in developing advanced materials, supporting the Green Deal's goal of industrial decarbonization and societal well-being. This initiative leverages materials modeling and characterization to create resilient new materials and discover novel uses for existing ones. By examining the relationships between processes, microstructure, and material properties, MatCHMaker helps predict and optimize material performance. Essential techniques, like Scanning Electron Microscopy (SEM), provide critical data on microstructures, which are analyzed using machine learning methods, including Convolutional Neural Networks and Autoencoders, to automate SEM image analysis. Focused on construction, energy, and mobility, the project integrates these ML tools to forecast materials' physical properties, ultimately developing a comprehensive materials characterization tool for industrial application. In this work, we present the progress done using ML methods to predict physical properties based on microstructure data. |