From the 2 of 15 linked papers with an AI index.
15 papers
Computing binary alloy phase diagrams with explicit configurational and vibrational entropy
Sarath Menon, Marvin Poul, Tilmann Hickel +2
The paper introduces a combined non‑equilibrium thermodynamic integration, Monte Carlo identity exchange, and molecular dynamics approach to compute binary alloy phase diagrams tha…
Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity
Nore Stolte, Harald Forbert, Yury Lysogorskiy +2
The paper presents a data‑efficient machine‑learning interatomic potential, trained on CCSD(T) data, that accurately captures the balance of π‑hydrogen bonding and hydrophobic solv…
A general-purpose atomic cluster expansion interatomic potential for niobium
Aleksei Egorov, Ralf Drautz, Thomas Hammerschmidt
Niobium, a body-centered cubic transition metal, poses a challenge for interatomic potentials, which struggle to capture its properties, such as phonons, high-pressure behavior, en…
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…
Hydride formation and phase separation in palladium nanoparticles from a transferable atomic cluster expansion potential
Minaam Qamar, Apinya Ngoipala, Matous Mrovec +2
The palladium-hydrogen system is a prototype for hydrogen-metal interactions and underpins technologies such as hydrogen storage, catalysis and purification. Yet its nanoscale beha…
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…