7 citations · 10 across the 5 of their papers we have counts for
5 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…
Understanding and Optimizing the Sensitization of Anatase Titanium Dioxide Surface with Hematite Clusters
Kati Asikainen, Matti Alatalo, Marko Huttula +2
The presence of small hematite (Fe2O3) clusters at low coverage on titanium dioxide (TiO2) surface has been observed to enhance photocatalytic activity, while excess loading of hem…