The start
On April 1, 2023, the groundbreaking initiative “Artificial Intelligence-Driven Materials Design for Spintronic Applications” (AI4SPIN) was launched. This ambitious project introduces a novel computational workflow, harnessing the power of artificial intelligence and bio-inspired algorithms. Our aim? To innovatively combine two-dimensional materials, creating new materials finely tuned for spintronic applications. In particular ultraefficient nonvolatile magnetic random access memories (MRAMs).
At the heart of our project lies the remarkable ability to modulate the electronic properties of two-dimensional material heterostructures. By expertly stacking, stretching, or twisting their layers, we unlock unprecedented opportunities. For instance, we can control the magnetism, and spin-orbit coupling of the materials, which is key to overcoming one of spintronics’ most enduring obstacles: the efficient electric control of spin.
The initial team
Principal Investigator
Jose H. is a computational physicist with broad experience in HPC algorithms in multi-core and GPU-CUDA platforms, data processing, machine learning, and physics. He has almost a decade of experience in the creation of efficient algorithms for quantum transport simulations and its applications to spintronics in low-dimensional materials.
Senior Researcher
Aron is a Senior Staff member at ICN2. His
research focuses on simulating the transport of charge, spin, and heat in nano- and low-dimensional materials, such as semiconductor nanowires, carbon nanotubes, graphene, and other 2D materials.
Stephan is an ICREA professor working at ICN2. He leads the “Theoretical and Computational Nanoscience” group which focuses on physics of Dirac materials (graphene & topological insulators) and 2D materials-based van der Waals heterostructures. He pioneered the development of linear scaling quantum transport approaches enabling simulations of billion atoms-scale disordered models (LSQUANT).
