7 papers
Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
Anton Bochkarev, Yury Lysogorskiy, Ralf Drautz
We present an algorithm for evaluating contracted Clebsch--Gordan tensor products in -equivariant machine learning potentials at fixed Canonical P…
AI-Driven Expansion and Application of the Alexandria Database
Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck +9
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stabili…
Hydrogen uptake and hydride formation in AlCoCrFeNi high-entropy alloys: First-principles, universal-potential, and experimental study
Fritz Körmann, Yuji Ikeda, Konstantin Glazyrin +9
Hydrogen uptake in complex multicomponent alloys, including high-entropy alloys (HEAs), governs both hydrogen storage capacity and resistance to hydrogen-induced degradation. We co…
Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions
Anton Bochkarev, Yury Lysogorskiy, Aparna Subramanyam +2
Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an…
Graph atomic cluster expansion for foundational machine learning interatomic potentials
Yury Lysogorskiy, Anton Bochkarev, Ralf Drautz
Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We intro…
Efficient local atomic cluster expansion for BaTiO close to equilibrium
Anna Grünebohm, Matous Mrovec, Maxim N. Popov +4
Barium titanate (BTO) is a representative perovskite oxide that undergoes three first-order ferroelectric phase transitions related to exceptional functional properties. In this wo…