Mapping out low-cost and earth-abundant substitutes for silver using machine learning: chartering a path to greater sustainability (SuSML)

Every year 1-2 materials are added to the Critical Raw Materials (CRMs) list, which are strategically important for the European economy, but have a high-risk associated with their supply. Demand for Silver, an electronic material, has skyrocketed in recent years due to less supply. Hence there is urgent need to find suitable alternatives for silver and to strengthen sustainable material ecosystem to increase our resilience, and reduce dependencies. This project combines innovations in machine learning with materials science to design fit-to-purpose materials. The project is highly interdisciplinary spanning across materials science, computer science and electronic engineering to explore earth-abundant elements to replace silver in electronics using machine learning approach.