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Showing 1–6 of 6 results
Advanced filters: Author: Albert Musaelian Clear advanced filters

  • Simon Batzner, Albert Musaelian and Boris Kozinsky discuss how exploiting the symmetry of Euclidean space can help tackle challenges in molecular simulations.

    • Simon Batzner
    • Albert Musaelian
    • Boris Kozinsky
    Comments & Opinion
    Nature Reviews Physics
    Volume: 5, P: 437-438

  • The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.

    • Albert Musaelian
    • Simon Batzner
    • Boris Kozinsky
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-15

  • The authors introduce a machine-learning framework that predicts how materials respond to electric fields with quantum-level accuracy, capturing vibrational, dielectric, and ferroelectric behaviors up to the million-atom scale.

    • Stefano Falletta
    • Andrea Cepellotti
    • Boris Kozinsky
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Batatia and colleagues introduce a computational framework that combines message-passing networks with the atomic cluster expansion architecture and incorporates a many-body description of the geometry of molecular structures. The resulting models are interpretable and accurate.

    • Ilyes Batatia
    • Simon Batzner
    • Gábor Csányi
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 56-67