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Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2
Yanhui Zhang, Alessandro Lunghi, Stefano Sanvito
Determining thermal and physical quantities across a broad temperature domain, especially up to the ultra-high temperature region, is a formidable theoretical and experimental chal…
Surfing multiple conformation-property landscapes via machine learning: Designing magnetic anisotropy
Alessandro Lunghi, Stefano Sanvito
The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computationa…
Edge superconductivity in Multilayer WTe2 Josephson junction
Ce Huang, Awadhesh Narayan, Enze Zhang +7
WTe2, as a type-II Weyl semimetal, has 2D Fermi arcs on the (001) surface in the bulk and 1D helical edge states in its monolayer. These features have recently attracted wide atten…
Predicting the Curie temperature of ferromagnets using machine learning
James Nelson, Stefano Sanvito
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High…
The nanoscale structure of the Pt-water double layer under bias revealed
Remi Khatib, Ashwinee Kumar, Stefano Sanvito +2
The nanoscopic mass and charge distribution within the double layer at electrified interfaces plays a key role in electrochemical phenomena of huge technological relevance for ener…
Interface engineering of graphene nanosheet reinforced ZrB composites by tuning surface contacts
Yanhui Zhang, Stefano Sanvito
The mechanical properties of heterophase interfaces are critically important for the behaviour of graphene-reinforced composites. In this work, the structure, adhesion, cleavage an…