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Showing 1–10 of 10 results
Advanced filters: Author: Simon Batzner 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

  • 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

  • A protocol using large-scale training of graph networks enables high-throughput discovery of novel stable structures and led to the identification of 2.2 million crystal structures, of which 381,000 are newly discovered stable materials.

    • Amil Merchant
    • Simon Batzner
    • Ekin Dogus Cubuk
    ResearchOpen Access
    Nature
    Volume: 624, P: 80-85

  • A biasing potential is derived from the uncertainty of a neural network ensemble and used to modify the potential energy surface in molecular dynamics simulations and facilitate the determination of underrepresented structural regions.

    • Simon Batzner
    News & Views
    Nature Computational Science
    Volume: 3, P: 190-191

  • This study introduces a2c, a computational method that leverages machine learning and atomistic simulations to predict the most likely crystallization products upon annealing of amorphous precursors. The a2c tool was demonstrated on a variety of materials, including oxides, nitrides and metallic glasses, and can assist researchers in discovering synthesis pathways for materials design.

    • Muratahan Aykol
    • Amil Merchant
    • Ekin Dogus Cubuk
    ResearchOpen Access
    Nature Computational Science
    Volume: 5, P: 105-111

  • Neural network force fields promise to bypass the computationally expensive quantum mechanical calculations typically required to investigate complex materials, such as lithium-ion batteries. Mailoa et al. accelerate these approaches with an architecture that exploits both rotation-invariant and -covariant features separately.

    • Jonathan P. Mailoa
    • Mordechai Kornbluth
    • Boris Kozinsky
    Research
    Nature Machine Intelligence
    Volume: 1, P: 471-479