107 citations · 299 across the 21 of their papers we have counts for
5 papers · 2 filters
Predicting properties of hard-coating alloys using ab-initio and machine learning methods
H. Levämäki, F. Tasnadi, D. G. Sangiovanni +3
Accelerated design of novel hard coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine lea…
Graph-based machine learning beyond stable materials and relaxed crystal structures
Filip Ekström, Rickard Armiento, Fredrik Lindsten
There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models…
Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining
Rhys E. A. Goodall, Abhijith S. Parackal, Felix A. Faber +2
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that mac…
Identification of materials with strong magneto-structural coupling using computational high-throughput screening
Luis Casillas-Trujillo, Rickard Armiento, Björn Alling
Important phenomena such as magnetostriction, magnetocaloric, and magnetoelectric effects arise from, or could be enhanced by, the coupling of magnetic and structural degrees of fr…
OPTIMADE, an API for exchanging materials data
Casper W. Andersen, Rickard Armiento, Evgeny Blokhin +53
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible an…