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Professor Stefano Sanvito

Professor of Condensed Matter Theory (Physics)
Director of CRANN (CRANN)
CRANN
      
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Professor Stefano Sanvito

Professor of Condensed Matter Theory (Physics)
CRANN

Director of CRANN (CRANN)

At the beginning of year 2002 I moved from University of California Santa Barbara to the Trinity College Dublin, creating the "Computational Spintronics Group" within the Physics Department. The main research focus of the group is to study theoretically spin transport at the nanscale level. The group at present comprises two postdoctoral researchers, six PhD students and several undergraduate and summer students. We established the "Computational Spintronics Lab." with nine state of the art PC workstations. The purchase of a large LinuX-based cluster is at present under tender. We maintain several collaborations locally and internationally with frequent visits to Dublin from our collaborators. In year 2003/2004 we host an equivalent of about two months visit. In the two years 2002-2004 we have published over ten publications in peer reviewed journals, two book chapters, several conference proceedings, and we have submitted an Irish patent. Recently we received fundings from Science Foundation of Ireland, Enterprise Ireland and the High Education Authority, for a total of about 1,5 Million Euro over the next five years. This ranks us as probably the largest and best funded single PI Computational Materials Science group in Ireland.
  Computer Modelling   Condensed Matter   Condensed Matter Theory   Electronic/Optical Materials   Magnetic Materials   Magnetics   Materials Sciences   Mathematical Modelling   Nanotechnology   Physics   Semiconductors   Theoretical Physics
Project Title
 Precise Scientific Information Extractor for accelerating materials discovery
From
25/09/2023
To
24/11/2023
Summary
The project aims at investigating the viability of a new spinout company that will develop a suit of natural language processing (NLP) algorithms for the precise extraction of materials properties from scientific literature. This will enable the creation of large datasets of materials properties and, most importantly, their interrelations, enabling the construction of large machine-learning (ML) models for materials design. The extracted data will be of a quality sufficient to enable downstream tasks, such as the construction of machine-learning models for materials design
Funding Agency
Enterprise Ireland
Programme
Feasibility Programme
Project Title
 InfoMatDesign: Precise Information Extraction through Natural Language Processing for Materials Design and Materials Market Intelligence
From
15/08/2024
To
14/08/2026
Summary
Precise Information Extraction through Natural Language Processing for Materials Design and Materials Market Intelligence
Funding Agency
Enterprise Ireland
Programme
Manufacturing, Energy and Food Commercialisation
Project Title
 ML-transport: machine-learning accelerators for quantum transport
From
02/09/2024
To
31/08/2028
Summary
In "ML-transport: machine-learning accelerators for quantum transport" we propose to combine machine-learning methods for predicting the charge density in a density functional theory (DFT) calculations with quantum transport theory. This will allow us to achieve a thousand-fold computational speedup, thus making the evaluation of finite-temperature transport possible
Funding Agency
Research Ireland
Programme
Government of Ireland Postgraduate Scholarship
Project Title
 Spin Transport and Relaxation Dynamics in Single-Molecule Junctions
From
01/07/2025
To
30/06/2027
Summary
Spin Transport and Relaxation Dynamics in Single-Molecule Junctions (STARDSMJ) aims at developing a toolbox of computational methods to study spin relaxation of molecular nanomagnets in the presence of charge fluctuations and external bias. This will allow us to investigate the factors affecting the spin relaxation in molecular magnets in realistic environments, namely in the operational conditions of nanoscale devices. Density Functional Theory combined with non-equilibrium Green's functions (NEGF) theory is the ab initio workhorse method for electron transport calculations. This, however, is limited to scattering from a static potential, while dynamical effects are typically included through appropriate self-energies in a perturbative way. In contrast, spin relaxation can be investigated by propagating in time master equations with parameters extracted from accurate electronic structure theory. In this case the molecule remains in a single charging state and inter-molecular interactions are of dipole-dipole nature. The main goal of STARDAM is to construct a formalism that can capture spin relaxation in an environment with fluctuating number of electrons, namely going beyond and unifying the two approaches. I will achieve this goal by developing and applying a robust theoretical scheme to solve the master equation for the transport of single-molecule junctions, where various effective spin Hamiltonians, extracted from first-principles calculations, will be used to describe the molecule.
Funding Agency
EU
Programme
HORIZON-MSCA-2024-PF-01
Project Type
HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
Project Title
 SSSLiP
From
01/04/2022
To
31/03/2026
Summary
Friction between moving parts and the associated wear are estimated to be directly responsible for 25% of the world's energy consumption. SSLiP seeks to establish a radically new way to drastically reduce friction, with potentially enormous technological and societal impact. The driving concept is structural superlubricity, extremely low friction that takes place at a lattice misfit between clean, flat, rigid crystalline surfaces. Structural superlubricity is currently a lab curiosity limited to micrometer scale and laboratory times. SSLiP will bring this to the macroscale to impact real-life products. The key idea is the use of tribo-colloids: colloidal particles coated in 2D materials, that will produce a dynamic network of superlubric contacts. Structural incompatibility between arrays of colloids allows us to replicate the low friction on bigger length scales and overcome the statistical roughness of real surfaces. Through careful design of these coatings, carrier fluid, and the mechanical properties of the core particles, the chemistry of sliding and collective behaviour of the colloids can be controlled. Synthesis and experiments of individual contacts will be combined with visualisation of colloid dynamics during sliding on larger scales and in-site chemical characterisation. These will be combined with multiscale simulations and theory to bridge the different length scales. The developed ultra-low friction technology will drastically reduce loss of energy, for example in passenger cars (responsible for around 2 billion tonnes of CO2 per year) and increase the lifetime of parts. It will also enable radically new technologies that are impossible with current lubrication.
Funding Agency
EU
Programme
Echorizon

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Italian Fluent Fluent Fluent
Details Date From Date To
Member of the Americal Physical Society
Organizer of the symposium on "spin injection into semiconductors" at JEMS 2004
Collaborator for Contemporary Physics
Reviewer for Physical Review B, Physical Review Letters, The European Physical Journal B, Europhysics Letters, Journal of Physics C: Condensed Matter, Semiconductor Science and Technology, Journal of Computational and Theoretical Nanoscience
Organizer of the "Magnetism and spin injection in semiconductors" symposium at the 2004 Joint European Magnetic Symposia (Dresden)
Member of the editorial board of "Journal of Computational and Theoretical Nanoscience"
Co-organizer of the "Spintronics" session at the 2003 Electronic Materials Conference
Declan Nell, Stefano Sanvito, Ivan Rungger, Andrea Droghetti, Effect of dynamical electron correlations on the tunnelling magnetoresistance of Fe/MgO/Fe(001) junctions, Physical Review B, 111, (3), 2025, Journal Article, PUBLISHED
Luke P. J. Gilligan, Matteo Cobelli, Valentin Taufour, Stefano Sanvito, Author Correction: A rule-free workflow for the automated generation of databases from scientific literature, npj Computational Materials, 10, (1), 2024, Journal Article, PUBLISHED  DOI  URL
Yudi Wang, Haoyang Pan, Yuxuan Jiang, Jie Li, Dongying Lin, Shi Li, Yongfeng Wang, Stefano Sanvito and Shimin Hou, High-performance molecular spin filters based on a square-planar four-coordinate Fe complex and covalent pyrazine anchoring groups, Journal of Materials Chemistry C, 12, 2024, p1297-1308 , Journal Article, PUBLISHED  DOI
Cian Gabbett, Luke Doolan, Kevin Synnatschke, Laura Gambini, Emmet Coleman, Adam G. Kelly, Shixin Liu, Eoin Caffrey, Jose Munuera, Catriona Murphy, Stefano Sanvito, Lewys Jones and Jonathan N. Coleman, Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography,, Nature Communications, 15, 2024, p278-, Journal Article, PUBLISHED  DOI
Hugo Rossignol, Michail Minotakis, Matteo Cobelli and Stefano Sanvito, Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams, Journal of Chemical Information and Modeling, 2024, Journal Article, PUBLISHED  DOI
El Tayeb Bentria, Prathamesh Mahesh Shenai, Stefano Sanvito, Heesoo Park, Laurent Karim Bland, Nicholas Laycock and Fedwa El Mellouhi, Computational demystification of Iron Carbonyls formation under gas reforming conditions, npj material degradation, npj Materials Degradation, 8, 2024, p19-, Journal Article, PUBLISHED  DOI
Hugo Rossignol, Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams, Journal of Chemical Information and Modeling, 2024, Notes: [https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c01391], Journal Article, PUBLISHED  TARA - Full Text  DOI
Bentria, E.T. and Shenai, P.M. and Sanvito, S. and Park, H. and Béland, L.K. and Laycock, N. and El Mellouhi, F., Computational demystification of iron carbonyls formation under syngas environment, npj Materials Degradation, 8, (1), 2024, Notes: [cited By 0], Journal Article, PUBLISHED  DOI
Bajaj, A. and Gupta, R. and Tokatly, I.V. and Sanvito, S. and Droghetti, A., Ab initio transport theory for the intrinsic spin Hall effect applied to 5d metals, Physical Review B, 109, (19), 2024, Notes: [cited By 0], Journal Article, PUBLISHED  DOI
Jiang, Y. and Li, S. and Wang, Y. and Sanvito, S. and Hou, S., Au-Thiolate Interfacial Coordination: The Key to Determining the Spin State of a Blatter Radical When Incorporated into Gold-Molecule-Gold Junctions, Journal of Physical Chemistry C, 128, (12), 2024, p5288-5299 , Notes: [cited By 0], Journal Article, PUBLISHED  DOI
  

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Peng Jiang, Lili Kang, Xiaohong Zheng, Zhi Zeng, and Stefano Sanvito, Computational prediction of a two-dimensional semiconductor SnO2 with negative Poisson's ratio and tunable magnetism by doping, Physical Review B, 2020, Journal Article, PUBLISHED
Aaron Hurley, Nadjib Baadji, Stefano Sanvito, A pertubative approach to the Kondo effect in magnetic atoms on nonmagnetic substrates, 2011, Journal Article, SUBMITTED
A. Filippetti, C. D. Pemmaraju, P. Delugas, D. Puggioni, V. Fiorentini, S. Sanvito, A variational pseudo-self-interaction correction approach: ab-initio description of correlated oxides and molecules, 2011, Journal Article, SUBMITTED

  


Award Date
Elected 'Cavaliere della Stella d'Italia', Knight of the Italian Star February 2017
Elected Member of the Royal Irish Academy May 2016
Fellow of the Institute of Physics (UK) April 2013
Fellow of Trinity College April 2006
2007 IUPAP Young Scientist Prize in Computational Physics September 2007
Spin-transport at the atomic scale Density functional study of electronic and transport properties of spintronics materials and in particular of III-V diluted magnetic semiconductors Development of analytical and numerical techniques for quantum electronic transport using ab initio methods Density functional theory methods for strongly correlated electrons Transport properties of magnetic hybrid systems and carbon nanotubes