Sampling Complex Energy Landscapes in Material Science Using Data-Driven Force Fields

Cosmin Marinica
April 12, 2024

AIMNet2 family of machine learning potentials: general-purpose and task-specific models for element-organic molecules and radicals, reactions and molecular crystals

Roman Zubatyuk
April 12, 2024

Machine Learning Interatomic Potentials to Predict Bond Dissociation Energies

Elena Gelzinyte
April 11, 2024

Charge and other advances in the Ch.ACE for next generation interatomic potentials

James Goff
April 11, 2024

Multiscale and Data-driven Methods for the Simulation of Materials Failure

James Kermode
April 11, 2024

Exploiting linear models for transferability and long-range interactions

Julien Lam
April 11, 2024

Machine learning at the mesoscale: advances in analyzing and coarse-graining via data-driven approaches

Rose Cersonsky
April 11, 2024

Cartesian atomic cluster expansion for machine learning interatomic potentials

Bingqing Cheng
April 10, 2024

Bayesian and Equivariant Force Fields for the Description of Metals in their Bulk, Surface, and Nano-Scale Forms

Cameron Owen
April 10, 2024