11 citations · 16 across the 3 of their papers we have counts for
4 papers
Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
Luis Casillas-Trujillo, Abhijith S. Parackal, Rickard Armiento +1
The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of ab-initio calcul…
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
Marian Arale Brännvall, Gabriel Persson, Luis Casillas-Trujillo +2
We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We…
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…
Interstitial Carbon in bcc HfNbTiVZr high entropy alloy from first principles
Luis Casillas-Trujillo, Ulf Jansson, Martin Sahlberg +6
The remarkable mechanical properties of high entropy alloys can be further improved by interstitial alloying. In this work we employ density functional theory calculations to study…