3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
Investigating the Electronic and Magnetic Properties of NaFeMnO Cathode Materials with X-ray Compton Scattering
Veenavee Nipunika Kothalawala, Kosuke Suzuki, Johannes Nokelainen +21
We discuss electronic and magnetic properties of NaFeMnO, a promising Na-ion battery cathode material. Using x-ray Compton scattering, SQUID magnetometry, a…
cond-mat.mtrl-sci2024
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…