17 citations · 27 across the 13 of their papers we have counts for
16 papers
A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials
Yury Lysogorskiy, Anton Bochkarev, Ralf Drautz
Foundation machine-learning interatomic potentials cover broad configurational and chemical spaces, but their reliability can vary across the atomic environments encountered during…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Reactive calcium carbonate precipitation from an atomic cluster expansion potential and enhanced sampling
Eslam Ibrahim, Yury Lysogorskiy, Ralf Drautz +1
Calcium carbonate formation from aqueous solution is central to biomineralization and to carbon sequestration through mineral carbonation. At near-neutral pH, the process is highly…
Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity
Nore Stolte, Harald Forbert, Yury Lysogorskiy +2
Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H hydrogen bonds, yet widely used force fields and state-of-t…
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…
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…
Water Phase Diagram from a General-Purpose Atomic Cluster Expansion Potential
Eslam Ibrahim, Yury Lysogorskiy, Ralf Drautz +1
Water's phase diagram remains one of the most intricate and challenging benchmarks in molecular modeling. In this study, we compute the phase diagram of water using an Atomic Clust…
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…
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…
Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography
Mariano Forti, Alesya Malakhova, Yury Lysogorskiy +5
Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly chal…
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…
Charge-constrained Atomic Cluster Expansion
Matteo Rinaldi, Anton Bochkarev, Yury Lysogorskiy +1
The atomic cluster expansion (ACE) efficiently parameterizes complex energy surfaces of pure elements and alloys. Due to the local nature of the many-body basis, ACE is inherently…
Development of an Atomic Cluster Expansion potential for iron and its oxides
Baptiste Bienvenu, Mira Todorova, Jörg Neugebauer +4
The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time sca…
Efficient parameterization of transferable Atomic Cluster Expansion for water
Eslam Ibrahim, Yury Lysogorskiy, Ralf Drautz
We present a highly accurate and transferable parameterization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach th…
From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll +10
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning P…