Brain Inspired AI Design of Topological Magnets for Sustainable Computing

Research question
This project aims to establish an advanced framework for predictive materials discovery, leveraging machine learning (ML) and brain-like neural networks (BLNNs) to target sustainable and functional magnetic materials. The focus is on identifying and characterizing novel topological magnets for neuromorphic computing paradigms that promote sustainability. The WISE-WASP connection is bringing a double benefit in addressing the research question, both by applying AI methods to materials design, and by designing materials enabling future computing paradigms.

Sustainability aspects
The soaring energy consumption of information technology urgently calls for the development of more energy-efficient information processing technology. One possible source of inspiration in addressing this crucial problem is the human brain, which is estimated to process information 1 million times more energy efficiently than currently available computers. The final goal of this project is to use efficient combinations of atomistic spin models and AI-based computational methods to optimize the properties of topological magnetic materials for neuromorphic or other unconventional computing. In particular, the project will investigate stabilization mechanisms and responses to external stimuli, including electrical currents (in metals) or applied electric fields (in insulators), magnetic fields, spin-orbit torques, mechanical deformations/strain fields, temperature, optical pulses, as well as gradients in previously mentioned fields. This will allow for identifying energy-efficient means of stabilizing and controlling the behavior of topological spin textures for next-generation computing applications