Computer design of new class of green magnets for energy applications and next-generation computing

Research Question

The demand for high-performance magnets is rising, but most rely on rare elements requiring environmentally harmful mining and energy-intensive production, and they pose recycling challenges. In contrast, organic-based magnets—though promising—typically function only below room temperature, limiting their applications. This project aims to identify organic systems with robust magnetism above room temperature through extensive first-principles simulations. By exploring how structural and chemical modifications affect magnetic properties, we’ll generate data to train physics-informed machine learning model. These models will help reverse-engineer promising compounds, which will then be validated via quantum-mechanical calculations and in collaboration with experimental groups. The proposed synergy between first-principles theory done by the WISE project members and physics-informed machine learning done by the WASP project members will be crucial for the feasibility of the search for new promising organic magnets due to ML-accelerated screening of candidate systems, which would otherwise be impossible using only first-principles theory.

Sustainability Aspects

Magnetic materials are vital to technologies such as energy generation, electric motors, and data storage. However, today’s high-performance magnets often rely on rare elements that come with high environmental and energy costs, as well as recycling challenges. Organic magnets offer a promising alternative: they can be synthesized more easily and energy-efficiently using flexible chemical methods and abundant, low-cost elements like carbon, hydrogen, and oxygen as well as common metals like iron and manganese.