Digital design, synthesis, and properties of multicomponent disordered metallic materials

R.E. Ryltsev

Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences

Multicomponent disordered metallic systems—including high-entropy alloys and metallic glasses—have attracted significant attention due to their unique functional properties. However, their complex atomic structures and vast compositional space pose challenges for traditional design approaches. This talk highlights the critical role of machine learning-driven methods in predicting the properties of these materials and accelerating their discovery. A special focus will be placed on advanced atomistic simulation techniques based on machine learning interatomic potentials, which enable high-accuracy simulations of disordered systems. By integrating computational design with experimental validation, we demonstrate pathways to tailor novel materials with targeted performance. The discussion will address recent breakthroughs, remaining challenges, and future directions in the digital design of disordered metallic materials for applications in advanced technologies.