Alessandro currently holds a Research Fellow position at the School of Physics, Trinity College Dublin, where he pursues a research activity at the boundary between computational chemistry, materials science and condensed-matter physics. He is specialized in the simulation of inorganic and magnetic materials with advanced electronic structure methods, molecular/spin dynamics and artificial intelligence techniques. His most recent interests involve the computational study of spin relaxation phenomenon at the molecular scale and the use of machine learning to design new compounds with tailored properties. On these topics, he has published several papers and developed new theories and computational algorithms. A full list of publications can be found at: Google Scholar
- E. Garlatti, L. Tesi, A. Lunghi, M. Atzori, D. Voneshen, P. Santini, S. Sanvito, T. Guidi, R. Sessoli, and S. Carretta. Unveiling phonons in a molecular quantum bit prototype with four-dimensional inelastic neutron scattering. Nature Communication 11, 1751 (2020).
- Alessandro Lunghi and Stefano Sanvito. Surfing multiple conformation-property landscapes via machine learning: Designing magnetic anisotropy. J. Chem. Phys. C 124, 5802–5806 (2019).
- Alessandro Lunghi and Stefano Sanvito. How do phonons relax molecular spins? Science Advances 5, eaax7163 (2019).
- Alessandro Lunghi and Stefano Sanvito, A unified picture of the covalent bond within quantum-accurate force fields: from simple organic molecules to metallic complexes’ reactivity, Science Advances 5, eaaw2210 (2019).
- Giant spin–phonon bottleneck effects in evaporable vanadyl-based molecules with long spin coherence, L. Tesi, A. Lunghi, M. Atzori, E. Lucaccini, L. Sorace, F. Totti and R. Sessoli,Dalton Trans. 45, 16635 (2016)
Full list of publication available at Google Scholar
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