The advancement of materials hinges on our ability to integrate experimental observations with computational approaches across multiple length and time scales. Bridging the gap between atomistic models, mesoscale phenomena, and macroscopic material behavior requires robust frameworks for data fusion, validation, and predictive modeling. In light of the surge of data-driven approaches in materials science, the ability to efficiently integrate all available, heterogeneous data sources through such a framework is not only a theoretical consideration, but of very practical importance for the applicability of machine learning and artificial intelligence approaches.
The 8th Materials Chain International Conference "Integrating experimental and simulation data across scales in materials science " brings together researchers at the forefront of materials characterization, computational modeling, and data science.
