Assistant Professor in the School of Physics 
Trinity College, Dublin 

Having pioneered the field of ab-initio spin dynamics simulations for magnetic semiconductors, Alessandro Lunghi is now spearheading the development of machine-learning strategies for designing new magnetic materials for quantum technology applications. In particular, Lunghi has devoted significant efforts to studying magnetic molecules as potential elements of quantum devices and classical high-density magnetic memories. During his Ph.D., he has been the main driver of developing a computational ab-initio strategy for the prediction of spin-phonon relaxation. From explaining the physics of spin relaxation to providing new guidelines for synthesizing new compounds, Lunghi’s results have effectively changed the conceptualization of spin relaxation in localized magnetic systems. His plans for the future aim at revolutionizing current design strategies for magnetic molecules with application in the space of quantum information science. Lunghi is planning to use machine learning and high-throughput simulations to efficiently explore the chemical space of coordination compounds in search of materials with outstanding quantum coherence.