Professor Alessandro De Vita
Telephone: +44 020 7848 2715
Research Group: Theory & Simulation of Condensed Matter
Alessandro has over twenty years experience in materials modelling, which started during the final year of his Physics MSc in Trieste, Italy, and developed interdisciplinary spanning over Physics and Materials Engineering, with a PhD in either discipline. During his career he has been working in various UK and EU institutions, including the Universities of Keele, Oxford and Cambridge, the Swiss Federal Institute of Technology (EPFL) in Lausanne, and the University of Trieste. In 2010 he became Professor of Physics at King’s College London, where since 2011 he serves as Chair of the Assessment Board (Postgraduate) of the Faculty of Natural and Mathematical Sciences. He is a Scientific Advisory Committee member of the EU Psi-K Network for which has been WG5-“Hybrid Classical and Quantum Methods” Spokesman (2003-2008). He is the KCL Director and former Chair of the Thomas Young London Centre for Theory and Simulation of Materials, which he co-founded in 2006. In 2012 he co-founded the UK-JCMaxwell CECAM Node, coordinating EU-wide scientific events organised by the London Colleges and the Universities of Oxford and Cambridge.
Professor Alessandro De Vita at the Thomas Young Centre
Alessandro’s research interests are centred on the development and application of atomistic modelling techniques involving massively parallel computing, dynamical database generation and machine learning. He uses these techniques to investigate materials’ chemo-mechanical properties such as their resistance to fracture and stress corrosion, as well as nanofabrication approaches such as supramolecular self-assembly. In the late 1990’s, he notably pioneered the “Learn On The Fly” (LOTF) multi-scale modelling scheme. The LOTF scheme was later on developed with support from EU-FP-funding, and applied to industrially relevant problems in a wider EU context. The method is currently being further developed to include on-the-fly force learning of first-principle forces from databases, using Bayesian techniques.
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- Gleizer et al., Phys. Rev. Lett. 112, 115501 (2014).
- J.R. Kermode et al., Nature Comm. 4, 2441 (2013).
- Floris et al., ACS Nano 7, 8059, (2013).
- N. Abdurakhmanova, et al., Nature Comm. 3:940 doi: 10.1038/ncomms1942 (2012).
- L.Vitali, et al., Nature Materials 9, 320 (2010).
- G.Moras et al., Phys. Rev. Lett. 105, 075502 (2010)
- D.Fernandez-Torre et al., Phys. Rev. Lett. 105, 185502 (2010).
- J.R. Kermode, et al., Nature 455, 1224 (2008).
- M.Lingenfelder et al., Angew. Chemie, Int. Ed. 46, 1, (2007) (issue cover).
- G.Csanyi, et al., Phys. Rev. Lett. 93, 175503 (2004).