3 papers
cond-mat.mtrl-sci2026
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
Abhijith S Parackal, Rickard Armiento, Florian Trybel
Machine learned interatomic potentials (MLIPs) are becoming a standard method for DFT-level accurate molecular dynamics simulation and large-scale studies of crystal energetics. In…
cond-mat.mtrl-sci2026
Screening 39 billion protostructures for materials discovery
Abhijith S Parackal, Florian Trybel, Felix Andreas Faber +1
Large-scale computational surveys are increasingly used to map the landscape of stable crystalline materials. We report a high-throughput energy screening of inorganic crystals tha…
cond-mat.mtrl-sci2025
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal +3
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its…