Armiento, Rickard

1. Crystal Structure Representations for Machine Learning Models of\n Formation Energies
   2015/01/01 by Felix A. Faber, Faber, Felix, Alexander Lindmaa +5 · 3 citations
   Computer Science · Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Physical sciences #Machine Learning in Materials Science #X-ray Diffraction in Crystallography
2. WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
   2025/02/10 by Filip Ekström Kelvinius, Oskar B. Andersson, Kelvinius, Filip Ekström +9 · 11 citations
   Materials Science · #Artificial Intelligence (cs.AI) #Crystallization and Solubility Studies #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)
3. Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining
   2021/06/21 by Goodall, Rhys E. A., Parackal, Abhijith S., Faber, Felix A. +2 · 2 citations
   #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)
4. Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
   2024/06/25 by Casillas-Trujillo, Luis, Parackal, Abhijith S., Armiento, Rickard +1 · 4 citations
   #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)
5. Theoretical characterization of NV-like defects in 4H-SiC using ADAQ with the SCAN and r2SCAN meta-GGA functionals
   2025/01/13 by Abbas, Ghulam, Bulancea-Lindvall, Oscar, Davidsson, Joel +2 · 2 citations
   #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)