4 papers
Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO Monolayer
Kati Asikainen, Matti Alatalo, Marko Huttula +1
Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can…
Suppression of stripe-ordered structural phases in monolayer IrTe by a gold substrate
Kati Asikainen, Frédéric Chassot, Baptiste Hildebrand +12
Metal-assisted exfoliation of two-dimensional (2D) materials has emerged as an efficient route to isolating large-area monolayer crystals, yet the influence of the supporting metal…
Tuning the Electronic Properties of Two-Dimensional Lepidocrocite Titanium Dioxide Based Heterojunctions
Kati Asikainen, Matti Alatalo, Marko Huttula +1
Two-dimensional (2D) heterostructures reveal novel physicochemical phenomena at different length scales, that are highly desirable for technological applications. We present a comp…
Tailoring the electronic properties of TiO monolayers for solar driven catalysis through transition metal doping
Kati Asikainen, Matti Alatalo, Marko Huttula +1
Substitutional doping with transition metals is carried out in the Lepidocrocite phase - the stable monolayer geometry of TiO, using density functional theory (DFT) methods. Th…