Session

DescriptionThe transition to a sustainable society critically depends on the discovery of new materials with improved efficiency, durability, and reduced environmental footprint. Achieving this requires transformative advances in the way materials are conceived, enabling rational design paradigms where atomistic modeling and data-driven methods guide synthesis and characterization towards target properties. In-silico approaches can streamline this process, but computer-aided materials design remains a complex multiscale challenge involving phenomena across several spatiotemporal scales. Classical multiscale modeling connects the atomistic resolution at the nanoscale with the macroscopic performance of the final product by sequentially upscaling the fundamental quantities that control material behavior [1]. More recently, innovative strategies combining data mining of synthesis and characterization protocols (both in-silico and analytical) with machine learning regression models have emerged as powerful tools to optimize the synthesis of diverse materials classes [2]. References [1] M. Andersson et al. A general, microkinetic model for dissolution of simple silicate and aluminosilicate minerals and glasses as a function of ph and temperature. Chemical Geology, 2025. [2] J. Guo and P. Schwaller. Directly optimizing for synthesizability in generative molecular design using retrosynthesis models. Chemical Science, 2025.