20 citations · 25 across the 3 of their papers we have counts for
9 papers
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…
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…
An Ontology for the Materials Design Domain
Huanyu Li, Rickard Armiento, Patrick Lambrix
In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models…
Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design
Rickard Armiento
This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on…
Semi-Local Parameterization of the Electron Localization Function in Second-Order Density Gradients
Alexander Lindmaa, Joel Davidsson, Ann E. Mattsson +1
The electron localization function (ELF) is a universal measure of electron localization that allows for, e.g., an effective characterization of physical bonds in molecular and sol…